Stateful stream processing with Apache Flink
0 likes245 views
The document discusses stateful stream processing using Apache Flink, including its definition, capabilities, and types of state management (operator, keyed, broadcast, and queryable state). It details how Flink handles data streams both in real-time and from bounded sources, emphasizing its scalability, fault tolerance, and support for large states. Additionally, the document outlines different state backends (memory, file system, and rocksdb) and the checkpointing process, which allows for replaying data from specific points in the stream.
1 of 23
Download to read offline
Ad
Recommended
Unified Stream and Batch Processing with Apache Flink
Unified Stream and Batch Processing with Apache FlinkDataWorks Summit/Hadoop Summit The document discusses Apache Flink, an open source stream processing framework. It provides high throughput and low latency processing of both streaming and batch data. Flink allows for explicit handling of event time, stateful stream processing with exactly-once semantics, and high performance. It also supports features like windowing, sessionization, and complex event processing that are useful for building streaming applications.
Apache Flink Training: System Overview
Apache Flink Training: System OverviewFlink Forward This document provides an overview of Apache Flink, an open-source stream processing framework. It discusses Flink's capabilities in supporting streaming, batch, and iterative processing natively through a streaming dataflow model. It also describes Flink's architecture including the client, job manager, task managers, and various execution setups like local, remote, YARN, and embedded. Finally, it compares Flink to other stream and batch processing systems in terms of their APIs, fault tolerance guarantees, and strengths.
ElasticSearch at berlinbuzzwords 2010
ElasticSearch at berlinbuzzwords 2010Elasticsearch The document describes how to build an Amazon-like store using Elasticsearch. It shows how to index book and CD documents specifying the index name, type, and ID. It demonstrates searching across types and indices, and introduces the concept of indices representing different document types like books and CDs. The document provides examples of indexing, searching, and retrieving documents from Elasticsearch.
Infrastructure-as-Code (IaC) using Terraform
Infrastructure-as-Code (IaC) using TerraformAdin Ermie The document provides an introduction to Infrastructure as Code (IaC) using Terraform, highlighting its benefits like reproducible environments and automation. It covers the basics of Terraform, including commands, state management, resource configuration, and best practices for using Terraform with Azure. Additionally, it offers learning resources, certification guidance, and comparisons between Terraform and Azure Resource Manager (ARM).
Git interview questions | Edureka
Git interview questions | EdurekaEdureka! This document contains 50 interview questions related to Git for beginner, intermediate, and advanced levels. It covers topics such as basic Git commands, branching strategies, resolving conflicts, rebasing vs merging, and integrating Git with Jenkins. For each question, it lists the question text and brief explanation or code snippets for the answer. The full document acts as a reference guide for learning and preparing for Git related technical interviews.
Deep dive into stateful stream processing in structured streaming by Tathaga...
Deep dive into stateful stream processing in structured streaming by Tathaga...Databricks The document provides an in-depth exploration of stateful stream processing within Spark's Structured Streaming framework, presented at the Spark Summit Europe 2017. It covers the architecture, including sources, sinks, transformations, and checkpointing, while explaining concepts such as watermarking for managing late data, streaming joins, and custom stateful operations using functions like mapGroupsWithState. The talk also discusses performance monitoring and debugging strategies for stateful queries in real-time processing applications.
Introduction to Kibana
Introduction to KibanaVineet . The document discusses various components of the ELK stack including Elasticsearch, Logstash, Kibana, and how they work together. It provides descriptions of each component, what they are used for, and key features of Kibana such as its user interface, visualization capabilities, and why it is used.
Apache Flink internals
Apache Flink internalsKostas Tzoumas This document provides an overview of Apache Flink internals. It begins with an introduction and recap of Flink programming concepts. It then discusses how Flink programs are compiled into execution plans and executed in a pipelined fashion, as opposed to being executed eagerly like regular code. The document outlines Flink's architecture including the optimizer, runtime environment, and data storage integrations. It also covers iterative processing and how Flink handles iterations both by unrolling loops and with native iterative datasets.
Enabling Vectorized Engine in Apache Spark
Enabling Vectorized Engine in Apache SparkKazuaki Ishizaki The document discusses enabling a vectorized engine in Apache Spark to improve performance through vectorization and SIMD (Single Instruction, Multiple Data) techniques. It covers limitations of the current Spark architecture regarding SIMD compatibility and introduces the Vector API as a solution for ensuring efficient SIMD code generation. The document also outlines various applications and comparisons of SIMD implementations, highlighting performance benchmarks between different approaches.
Where is my bottleneck? Performance troubleshooting in Flink
Where is my bottleneck? Performance troubleshooting in FlinkFlink Forward The document discusses performance troubleshooting in Apache Flink, highlighting the common bottlenecks in processing records, checkpointing, and recovery. It provides a step-by-step approach to identify and analyze performance issues, including the significance of analyzing machine metrics and leveraging profiling tools. Recommendations for improving checkpoint performance and recovery times are also outlined, emphasizing the importance of addressing backpressure and resource overload.
Fundamentals of Apache Kafka
Fundamentals of Apache KafkaChhavi Parasher The document presents an overview of Apache Kafka, describing it as a high throughput distributed messaging system designed for data pipelines and real-time event processing. It covers Kafka's architecture, terminologies, benefits such as scalability and durability, and its applications in various industries. Additionally, it discusses Kafka's production implementation at Zapr, including configurations, challenges, and tools for management and monitoring.
Virtual Flink Forward 2020: A deep dive into Flink SQL - Jark Wu
Virtual Flink Forward 2020: A deep dive into Flink SQL - Jark WuFlink Forward The document presents a comprehensive overview of Flink SQL, detailing its architecture and operational mechanisms before and after version 1.9, including various SQL optimizations and the integration of a changelog mechanism for stream processing. The discussion covers how Flink SQL operates through logical and physical plans and highlights recent advancements like the Blink planner which significantly improves performance. Future enhancements aim to unify batch and streaming processing with new interfaces and increased compatibility with Hive.
Introduction to Apache Kafka
Introduction to Apache KafkaJeff Holoman The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.
Beyond SQL: Speeding up Spark with DataFrames
Beyond SQL: Speeding up Spark with DataFramesDatabricks This document summarizes Spark SQL and DataFrames in Spark. It notes that Spark SQL is part of the core Spark distribution and allows running SQL and HiveQL queries. DataFrames provide a way to select, filter, aggregate and plot structured data like in R and Pandas. DataFrames allow writing less code through a high-level API and reading less data by using optimized formats and partitioning. The optimizer can optimize queries across functions and push down predicates to read less data. This allows creating and running Spark programs faster.
Spark streaming , Spark SQL
Spark streaming , Spark SQLYousun Jeong Spark Streaming allows processing of live data streams in Spark. It integrates streaming data and batch processing within the same Spark application. Spark SQL provides a programming abstraction called DataFrames and can be used to query structured data in Spark. Structured Streaming in Spark 2.0 provides a high-level API for building streaming applications on top of Spark SQL's engine. It allows running the same queries on streaming data as on batch data and unifies streaming, interactive, and batch processing.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks The document discusses designing ETL pipelines using Spark's structured streaming to handle real-time data processing efficiently. It outlines various design patterns for building streaming data pipelines, including handling unstructured input, key-value outputs, and joining multiple data streams while addressing common mistakes. Additionally, it emphasizes the use of Delta Lake for ACID transactions and implementing best practices for building robust streaming applications.
Nifi workshop
Nifi workshopYifeng Jiang Yifeng Jiang gives a presentation introducing Apache Nifi. He begins with an overview of himself and the agenda. He then provides an introduction to Nifi including terminology like FlowFile and Processor. Key aspects of Nifi are demonstrated including the user interface, provenance tracking, queue prioritization, cluster architecture, and a demo of real-time data processing. Example use cases are discussed like indexing JSON tweets and indexing data from a relational database. The presentation concludes that Nifi is an easy to use and powerful system for processing and distributing data with 90 built-in processors.
Parquet performance tuning: the missing guide
Parquet performance tuning: the missing guideRyan Blue Parquet performance tuning focuses on optimizing Parquet reads by leveraging columnar organization, encoding, and filtering techniques. Statistics and dictionary filtering can eliminate unnecessary data reads by filtering at the row group and page levels. However, these optimizations require columns to be sorted and fully dictionary encoded within files. Increasing dictionary size thresholds and decreasing row group sizes can help avoid dictionary encoding fallback and improve filtering effectiveness. Future work may include new encodings, compression algorithms like Brotli, and page-level filtering in the Parquet format.
Changelog Stream Processing with Apache Flink
Changelog Stream Processing with Apache FlinkFlink Forward The document discusses Apache Flink, a powerful tool for stream processing and changing data collection, highlighting its APIs and features used in handling streams and dynamic tables. It covers how data processing is fundamentally a stream of changes, how Flink SQL operates with these streams, and the implications of using different changelog modes. Additionally, it introduces the changelog insights and the flexibility of integrating with various data systems, ultimately summarizing Flink's capabilities as a changelog processor.
kafka
kafkaAmikam Snir Kafka is an open-source distributed commit log service that provides high-throughput messaging functionality. It is designed to handle large volumes of data and different use cases like online and offline processing more efficiently than alternatives like RabbitMQ. Kafka works by partitioning topics into segments spread across clusters of machines, and replicates across these partitions for fault tolerance. It can be used as a central data hub or pipeline for collecting, transforming, and streaming data between systems and applications.
Can Apache Kafka Replace a Database?
Can Apache Kafka Replace a Database?Kai Wähner The document discusses whether Apache Kafka can replace a traditional database, concluding that while Kafka can serve as a durable storage solution with ACID guarantees, it does not entirely replace databases but rather complements them. Kafka's capabilities include robust data storage, event streaming, and real-time processing, making it a powerful tool alongside existing database systems. Key features like tiered storage and ksqlDB enhance Kafka's usability for various applications while maintaining integration with traditional databases.
Terraform
TerraformPhil Wilkins The document provides an overview of Terraform and discusses why it was chosen over other infrastructure as code tools. It outlines an agenda covering Terraform installation, configuration, and use of data sources and resources to build example infrastructure including a VCN, internet gateway, subnets, and how to taint and destroy resources. The live demo then walks through setting up Terraform and using it to provision example OCI resources.
Batch Processing at Scale with Flink & Iceberg
Batch Processing at Scale with Flink & IcebergFlink Forward The document outlines Goldman Sachs' batch processing framework using Flink and Iceberg to create scalable data pipelines with a focus on decoupling producers and consumers. It discusses metadata management, merging strategies, and performance optimizations that support various data consumption patterns while ensuring efficient data access and retention. The implementation leverages Iceberg's capabilities for metadata handling and improved query performance, highlighting the benefits of modern batch processing strategies.
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...Flink Forward The document outlines the design and manual migration steps for multi-cluster Kafka source in Apache Flink, detailing the necessary tasks for swapping and upgrading clusters while minimizing downtime. It highlights the challenges of scaling multiple Kafka clusters across hybrid cloud environments, emphasizing complex operability and failover issues. The document also discusses benefits such as automated migrations and future work opportunities regarding watermark alignment and optimizations.
Accelerating Data Ingestion with Databricks Autoloader
Accelerating Data Ingestion with Databricks AutoloaderDatabricks The document discusses Databricks Autoloader, an optimized cloud file source for Apache Spark that enables efficient incremental data ingestion from cloud storage. It highlights the components, implementation methods, and schema evolution features, emphasizing the advantages of using Autoloader over existing ingestion patterns. Additionally, it provides insights into practical implementations, lessons learned, and best practices for managing event grids and streaming operations.
Introduction to elasticsearch
Introduction to elasticsearchhypto The document provides an introduction to Elasticsearch, its architecture, and associated tools like Kibana and Logstash. It covers key concepts such as indices, documents, clustering, and relevance models, while also discussing practical advice for using Elasticsearch effectively. Additionally, a demonstration using Kibana and insights into the company's revenue model are included.
Dataflow with Apache NiFi
Dataflow with Apache NiFiDataWorks Summit/Hadoop Summit This document provides an overview of Apache NiFi and dataflow. It begins with an introduction to the challenges of moving data effectively within and between systems. It then discusses Apache NiFi's key features for addressing these challenges, including guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document outlines NiFi's architecture and components like repositories and extension points. It also previews a live demo and invites attendees to further discuss Apache NiFi at a Birds of a Feather session.
From Zero to Hero with Kafka Connect
From Zero to Hero with Kafka Connectconfluent The document serves as a practical guide for becoming proficient with Kafka Connect, detailing its integration capabilities with various data sources and sinks such as Amazon S3 and PostgreSQL. It includes instructions for configuring connectors, handling serialization, applying transformations, deploying connectors, and troubleshooting common issues. Additionally, it highlights topics like scaling, fault tolerance, and error handling with dead letter queues.
Zurich Flink Meetup
Zurich Flink MeetupKonstantinos Kloudas The document discusses stateful stream processing with Apache Flink, highlighting its capability to handle continuously arriving data for accurate real-time results. It addresses the challenges of traditional batch processing, such as state and time handling, and presents Flink's solutions for fault tolerance and efficiency. Ultimately, it emphasizes Flink's sophisticated nature as a stateful stream processor that simplifies complex data management for developers.
Introduction to Stateful Stream Processing with Apache Flink.
Introduction to Stateful Stream Processing with Apache Flink.Konstantinos Kloudas Kostas Kloudas presented on stateful stream processing with Apache Flink. He discussed how Flink handles state management, fault tolerance, and time semantics to allow for continuous and accurate processing of streaming data. Flink embeds local state with keyed streams, takes consistent snapshots of distributed state, and uses watermarks to process events in event time to produce correct results even for out-of-order data. This allows Flink to provide a robust stream processing engine that scales to large deployments.
More Related Content
What's hot (20)
Enabling Vectorized Engine in Apache Spark
Enabling Vectorized Engine in Apache SparkKazuaki Ishizaki The document discusses enabling a vectorized engine in Apache Spark to improve performance through vectorization and SIMD (Single Instruction, Multiple Data) techniques. It covers limitations of the current Spark architecture regarding SIMD compatibility and introduces the Vector API as a solution for ensuring efficient SIMD code generation. The document also outlines various applications and comparisons of SIMD implementations, highlighting performance benchmarks between different approaches.
Where is my bottleneck? Performance troubleshooting in Flink
Where is my bottleneck? Performance troubleshooting in FlinkFlink Forward The document discusses performance troubleshooting in Apache Flink, highlighting the common bottlenecks in processing records, checkpointing, and recovery. It provides a step-by-step approach to identify and analyze performance issues, including the significance of analyzing machine metrics and leveraging profiling tools. Recommendations for improving checkpoint performance and recovery times are also outlined, emphasizing the importance of addressing backpressure and resource overload.
Fundamentals of Apache Kafka
Fundamentals of Apache KafkaChhavi Parasher The document presents an overview of Apache Kafka, describing it as a high throughput distributed messaging system designed for data pipelines and real-time event processing. It covers Kafka's architecture, terminologies, benefits such as scalability and durability, and its applications in various industries. Additionally, it discusses Kafka's production implementation at Zapr, including configurations, challenges, and tools for management and monitoring.
Virtual Flink Forward 2020: A deep dive into Flink SQL - Jark Wu
Virtual Flink Forward 2020: A deep dive into Flink SQL - Jark WuFlink Forward The document presents a comprehensive overview of Flink SQL, detailing its architecture and operational mechanisms before and after version 1.9, including various SQL optimizations and the integration of a changelog mechanism for stream processing. The discussion covers how Flink SQL operates through logical and physical plans and highlights recent advancements like the Blink planner which significantly improves performance. Future enhancements aim to unify batch and streaming processing with new interfaces and increased compatibility with Hive.
Introduction to Apache Kafka
Introduction to Apache KafkaJeff Holoman The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.
Beyond SQL: Speeding up Spark with DataFrames
Beyond SQL: Speeding up Spark with DataFramesDatabricks This document summarizes Spark SQL and DataFrames in Spark. It notes that Spark SQL is part of the core Spark distribution and allows running SQL and HiveQL queries. DataFrames provide a way to select, filter, aggregate and plot structured data like in R and Pandas. DataFrames allow writing less code through a high-level API and reading less data by using optimized formats and partitioning. The optimizer can optimize queries across functions and push down predicates to read less data. This allows creating and running Spark programs faster.
Spark streaming , Spark SQL
Spark streaming , Spark SQLYousun Jeong Spark Streaming allows processing of live data streams in Spark. It integrates streaming data and batch processing within the same Spark application. Spark SQL provides a programming abstraction called DataFrames and can be used to query structured data in Spark. Structured Streaming in Spark 2.0 provides a high-level API for building streaming applications on top of Spark SQL's engine. It allows running the same queries on streaming data as on batch data and unifies streaming, interactive, and batch processing.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks The document discusses designing ETL pipelines using Spark's structured streaming to handle real-time data processing efficiently. It outlines various design patterns for building streaming data pipelines, including handling unstructured input, key-value outputs, and joining multiple data streams while addressing common mistakes. Additionally, it emphasizes the use of Delta Lake for ACID transactions and implementing best practices for building robust streaming applications.
Nifi workshop
Nifi workshopYifeng Jiang Yifeng Jiang gives a presentation introducing Apache Nifi. He begins with an overview of himself and the agenda. He then provides an introduction to Nifi including terminology like FlowFile and Processor. Key aspects of Nifi are demonstrated including the user interface, provenance tracking, queue prioritization, cluster architecture, and a demo of real-time data processing. Example use cases are discussed like indexing JSON tweets and indexing data from a relational database. The presentation concludes that Nifi is an easy to use and powerful system for processing and distributing data with 90 built-in processors.
Parquet performance tuning: the missing guide
Parquet performance tuning: the missing guideRyan Blue Parquet performance tuning focuses on optimizing Parquet reads by leveraging columnar organization, encoding, and filtering techniques. Statistics and dictionary filtering can eliminate unnecessary data reads by filtering at the row group and page levels. However, these optimizations require columns to be sorted and fully dictionary encoded within files. Increasing dictionary size thresholds and decreasing row group sizes can help avoid dictionary encoding fallback and improve filtering effectiveness. Future work may include new encodings, compression algorithms like Brotli, and page-level filtering in the Parquet format.
Changelog Stream Processing with Apache Flink
Changelog Stream Processing with Apache FlinkFlink Forward The document discusses Apache Flink, a powerful tool for stream processing and changing data collection, highlighting its APIs and features used in handling streams and dynamic tables. It covers how data processing is fundamentally a stream of changes, how Flink SQL operates with these streams, and the implications of using different changelog modes. Additionally, it introduces the changelog insights and the flexibility of integrating with various data systems, ultimately summarizing Flink's capabilities as a changelog processor.
kafka
kafkaAmikam Snir Kafka is an open-source distributed commit log service that provides high-throughput messaging functionality. It is designed to handle large volumes of data and different use cases like online and offline processing more efficiently than alternatives like RabbitMQ. Kafka works by partitioning topics into segments spread across clusters of machines, and replicates across these partitions for fault tolerance. It can be used as a central data hub or pipeline for collecting, transforming, and streaming data between systems and applications.
Can Apache Kafka Replace a Database?
Can Apache Kafka Replace a Database?Kai Wähner The document discusses whether Apache Kafka can replace a traditional database, concluding that while Kafka can serve as a durable storage solution with ACID guarantees, it does not entirely replace databases but rather complements them. Kafka's capabilities include robust data storage, event streaming, and real-time processing, making it a powerful tool alongside existing database systems. Key features like tiered storage and ksqlDB enhance Kafka's usability for various applications while maintaining integration with traditional databases.
Terraform
TerraformPhil Wilkins The document provides an overview of Terraform and discusses why it was chosen over other infrastructure as code tools. It outlines an agenda covering Terraform installation, configuration, and use of data sources and resources to build example infrastructure including a VCN, internet gateway, subnets, and how to taint and destroy resources. The live demo then walks through setting up Terraform and using it to provision example OCI resources.
Batch Processing at Scale with Flink & Iceberg
Batch Processing at Scale with Flink & IcebergFlink Forward The document outlines Goldman Sachs' batch processing framework using Flink and Iceberg to create scalable data pipelines with a focus on decoupling producers and consumers. It discusses metadata management, merging strategies, and performance optimizations that support various data consumption patterns while ensuring efficient data access and retention. The implementation leverages Iceberg's capabilities for metadata handling and improved query performance, highlighting the benefits of modern batch processing strategies.
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...Flink Forward The document outlines the design and manual migration steps for multi-cluster Kafka source in Apache Flink, detailing the necessary tasks for swapping and upgrading clusters while minimizing downtime. It highlights the challenges of scaling multiple Kafka clusters across hybrid cloud environments, emphasizing complex operability and failover issues. The document also discusses benefits such as automated migrations and future work opportunities regarding watermark alignment and optimizations.
Accelerating Data Ingestion with Databricks Autoloader
Accelerating Data Ingestion with Databricks AutoloaderDatabricks The document discusses Databricks Autoloader, an optimized cloud file source for Apache Spark that enables efficient incremental data ingestion from cloud storage. It highlights the components, implementation methods, and schema evolution features, emphasizing the advantages of using Autoloader over existing ingestion patterns. Additionally, it provides insights into practical implementations, lessons learned, and best practices for managing event grids and streaming operations.
Introduction to elasticsearch
Introduction to elasticsearchhypto The document provides an introduction to Elasticsearch, its architecture, and associated tools like Kibana and Logstash. It covers key concepts such as indices, documents, clustering, and relevance models, while also discussing practical advice for using Elasticsearch effectively. Additionally, a demonstration using Kibana and insights into the company's revenue model are included.
Dataflow with Apache NiFi
Dataflow with Apache NiFiDataWorks Summit/Hadoop Summit This document provides an overview of Apache NiFi and dataflow. It begins with an introduction to the challenges of moving data effectively within and between systems. It then discusses Apache NiFi's key features for addressing these challenges, including guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document outlines NiFi's architecture and components like repositories and extension points. It also previews a live demo and invites attendees to further discuss Apache NiFi at a Birds of a Feather session.
From Zero to Hero with Kafka Connect
From Zero to Hero with Kafka Connectconfluent The document serves as a practical guide for becoming proficient with Kafka Connect, detailing its integration capabilities with various data sources and sinks such as Amazon S3 and PostgreSQL. It includes instructions for configuring connectors, handling serialization, applying transformations, deploying connectors, and troubleshooting common issues. Additionally, it highlights topics like scaling, fault tolerance, and error handling with dead letter queues.
Similar to Stateful stream processing with Apache Flink (20)
Zurich Flink Meetup
Zurich Flink MeetupKonstantinos Kloudas The document discusses stateful stream processing with Apache Flink, highlighting its capability to handle continuously arriving data for accurate real-time results. It addresses the challenges of traditional batch processing, such as state and time handling, and presents Flink's solutions for fault tolerance and efficiency. Ultimately, it emphasizes Flink's sophisticated nature as a stateful stream processor that simplifies complex data management for developers.
Introduction to Stateful Stream Processing with Apache Flink.
Introduction to Stateful Stream Processing with Apache Flink.Konstantinos Kloudas Kostas Kloudas presented on stateful stream processing with Apache Flink. He discussed how Flink handles state management, fault tolerance, and time semantics to allow for continuous and accurate processing of streaming data. Flink embeds local state with keyed streams, takes consistent snapshots of distributed state, and uses watermarks to process events in event time to produce correct results even for out-of-order data. This allows Flink to provide a robust stream processing engine that scales to large deployments.
Tzu-Li (Gordon) Tai - Stateful Stream Processing with Apache Flink
Tzu-Li (Gordon) Tai - Stateful Stream Processing with Apache FlinkVerverica The document discusses stateful stream processing using Apache Flink, detailing its benefits over traditional batch processing. It highlights key concepts such as fault tolerance, state management, and event-time awareness, which are fundamental to achieving reliable and efficient stream processing. Additionally, it emphasizes Flink's capabilities in handling large state efficiently and its sophisticated APIs for developers.
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data ArtisansEvention The document discusses Apache Flink, a stateful stream processing framework, highlighting its capabilities for handling continuous data streams and fault tolerance. It covers the architecture, including the DataStream API and the ability to consistently snapshot state for fault tolerance. Recent updates since version 1.4 include improved support for exactly-once semantics and enhancements for managing large states efficiently.
2018-04 Kafka Summit London: Stephan Ewen - "Apache Flink and Apache Kafka fo...
2018-04 Kafka Summit London: Stephan Ewen - "Apache Flink and Apache Kafka fo...Ververica The document provides an overview of Apache Flink, a platform for stream processing that supports batch and real-time data processing. It discusses key features such as stateful computations, checkpointing for fault tolerance, and integration with Apache Kafka, emphasizing its scalability and low-latency capabilities for event-driven applications. The document also highlights various use cases and recent advancements in Flink, as well as its potential applications in machine learning and real-time optimization.
Marton Balassi – Stateful Stream Processing
Marton Balassi – Stateful Stream ProcessingFlink Forward This document discusses stateful stream processing. It provides examples of stateful streaming applications and describes several open source stream processors, including their programming models and approaches to fault tolerance. It also examines how different systems handle state in streaming programs and discusses the tradeoffs of various approaches.
Flink Meetup Septmeber 2017 2018
Flink Meetup Septmeber 2017 2018Christos Hadjinikolis The 2017-18 Apache Flink Meetup Year Review highlights the growth of the Apache Flink community, which now has over 500 members and has conducted 17 successful meetups since its inception. Apache Flink is described as a framework for distributed processing of stateful computations over data streams, with various companies like Alibaba, eBay, and Uber employing it for diverse applications. The document also discusses last year's talks, advancements in Flink version 1.6.0, and future improvements planned for the framework, including better SQL integration and support for machine learning models.
Advanced Streaming Analytics with Apache Flink and Apache Kafka, Stephan Ewen
Advanced Streaming Analytics with Apache Flink and Apache Kafka, Stephan Ewenconfluent The document discusses streaming analytics using Apache Flink, highlighting its capabilities for real-time and batch processing through features like low latency, fault tolerance, and various time semantics. It covers the architecture, data flow, and examples of case classes for event processing, as well as techniques for stateful streaming and complex event processing. The document concludes with insights on future enhancements and use cases of Flink in large-scale data environments.
Stream processing - Apache flink
Stream processing - Apache flinkRenato Guimaraes Apache Flink is a distributed processing engine designed for stateful computations over both unbounded and bounded data streams, featuring low-latency, high throughput, and fault tolerance. It supports various applications such as fraud detection, real-time analytics, and data pipelines, employing several APIs like SQL, Table API, and DataStream API for stream processing. Flink's architecture includes job managers and task managers for efficient resource management and offers mechanisms like savepoints for fault tolerance and state management.
Apache Flink 101 - the rise of stream processing and beyond
Apache Flink 101 - the rise of stream processing and beyondBowen Li This document provides an overview and summary of Apache Flink. It discusses how Flink enables stateful stream processing and beyond. Key points include that Flink allows for stateful computations over event streams in an expressive, scalable, fault-tolerant way through layered APIs. It also supports batch processing, machine learning, and serving as a stream processor that unifies streaming and batch. The document highlights many use cases of Flink at Alibaba and how it powers critical systems like real-time analytics and recommendations.
Flink Streaming @BudapestData
Flink Streaming @BudapestDataGyula Fóra This document provides an overview of Apache Flink, an open-source platform for distributed stream and batch data processing. Flink allows for unified batch and stream processing with a simple yet powerful programming model. It features native stream processing, exactly-once fault tolerance based on consistent snapshots, and high performance optimized for streaming workloads. The document outlines Flink's APIs, state management, fault tolerance approach, and roadmap for continued improvements in 2015.
Flexible and Real-Time Stream Processing with Apache Flink
Flexible and Real-Time Stream Processing with Apache FlinkDataWorks Summit This document provides an overview of stream processing with Apache Flink. It discusses the rise of stream processing and how it enables low-latency applications and real-time analysis. It then describes Flink's stream processing capabilities, including pipelining of data, fault tolerance through checkpointing and recovery, and integration with batch processing. The document also summarizes Flink's programming model, state management, and roadmap for further development.
Apache Flink @ Tel Aviv / Herzliya Meetup
Apache Flink @ Tel Aviv / Herzliya MeetupRobert Metzger Apache Flink is an open-source stream processing framework designed for low latency and high throughput in event-driven applications. It provides building blocks for stateful computations over data streams and supports various use cases including streaming analytics, fraud detection, and machine learning. The document outlines key concepts, APIs, and implementation details relevant to leveraging Flink for real-time data processing.
Flink Forward SF 2017: Joe Olson - Using Flink and Queryable State to Buffer ...
Flink Forward SF 2017: Joe Olson - Using Flink and Queryable State to Buffer ...Flink Forward The document discusses the use of Apache Flink for managing high-frequency time series data collected from wearable patches, emphasizing its capability to create a predictive health model. It explains Flink's state management features, particularly managed keyed state, and how to access queryable state outside the Flink runtime. Various use cases illustrate the transition from batch processing to stream processing within the healthcare data management context.
Apache flink
Apache flinkpranay kumar Apache Flink is a distributed stream processing framework designed for handling large volumes of real-time data. It supports both batch and stream processing, offering features like low latency, high throughput, and fault tolerance through checkpointing. Flink's ecosystem comprises various data sources and sinks, and it enables users to perform complex operations on continuous data streams using APIs for dataset and datastream.
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords Stephan Ewen The document discusses Apache Flink as a stream processor that efficiently handles both real-time and historical data with features like low latency, high throughput, and exactly-once semantics for fault tolerance. It highlights the architecture, data processing capabilities, and the concept of queryable state, which allows applications to access real-time and historical results. The conclusion emphasizes that Apache Flink's sophisticated state management improves performance and mitigates bottlenecks in data processing systems.
The Stream Processor as a Database Apache Flink
The Stream Processor as a Database Apache FlinkDataWorks Summit/Hadoop Summit Apache Flink enables stream processing on continuously produced data through its DataStream and DataSet APIs. It allows for streaming and batch processing as first class citizens. Flink programs are composed of sources that ingest data, transformations on those data streams, and sinks that output the results. Queryable state in Flink allows for querying the system state without writing to an external database, improving performance over traditional architectures that rely on writing intermediate results to external key-value stores. Flink's use of lightweight snapshots for fault tolerance and its log-based approach to persistence allows queryable state to have high throughput and low latency.
Flink System Overview
Flink System OverviewTimo Walther Apache Flink is a stream processing framework that enables real-time data analysis and processing of continuous data streams with low latency and high throughput. It allows for stateful processing and accurate event-time handling, making it suitable for various applications such as real-time business monitoring and data analytics. The framework supports both real-time and historical data processing from a single application, enhancing the robustness of continuous applications.
Data Stream Processing with Apache Flink
Data Stream Processing with Apache FlinkFabian Hueske Apache Flink is an open-source platform designed for scalable stream and batch processing, offering low latency, high throughput, and exactly-once consistency. It supports complex event processing and handles out-of-order streams, providing an intuitive API similar to batch processing. With a vibrant community and robust operational features, Flink aims to simplify data infrastructure and enhance real-time analytics.
Flink Architecture
Flink Architecture Prasad Wali Flink is a framework for distributed stream and batch data processing. It defines workflows as directed acyclic graphs (DAGs) of tasks that can be distributed across worker nodes for scalability. It supports both bounded and unbounded streams, maintains fault tolerance through snapshots, and is capable of exactly-once processing through stream alignment during checkpoints. The master coordinates scheduling while workers execute tasks, and stateful operations are handled through windows that collect events over time or size thresholds.
Ad
More from Knoldus Inc. (20)
Angular Hydration Presentation (FrontEnd)
Angular Hydration Presentation (FrontEnd)Knoldus Inc. The document discusses Angular hydration, a process that transforms server-rendered HTML into a fully interactive Angular application. It highlights the importance of hydration for improved performance, SEO, and user experience while outlining the implementation steps and potential constraints that may arise. Key etiquette for participants during sessions is also provided, emphasizing punctuality and minimizing disruptions.
Optimizing Test Execution: Heuristic Algorithm for Self-Healing
Optimizing Test Execution: Heuristic Algorithm for Self-HealingKnoldus Inc. The document discusses the challenges of traditional test execution in software development and the importance of optimized processes for enhancing efficiency and reliability. It introduces self-healing techniques, particularly heuristic algorithms that enable automated test scripts to adapt to changes in application interfaces, thereby improving test coverage and minimizing false positives. Specific heuristic algorithms, such as adaptive locator recovery and annotation-based locators, are outlined with best practices for implementation in automated testing environments.
Self-Healing Test Automation Framework - Healenium
Self-Healing Test Automation Framework - HealeniumKnoldus Inc. The document discusses Healenium, a self-healing test automation framework that uses machine learning to automatically update broken locators during runtime, ensuring consistent test execution in dynamic environments. It emphasizes the importance of self-healing in reducing test failures, decreasing flakiness, and improving test accuracy, while providing an overview of its features, integration methods, and challenges. Future trends in self-healing automation are also highlighted, indicating a shift towards more proactive and sophisticated algorithms.
Kanban Metrics Presentation (Project Management)
Kanban Metrics Presentation (Project Management)Knoldus Inc. The document discusses kanban metrics and principles, emphasizing the importance of etiquette in sessions, including punctuality and giving feedback. It details the history of kanban, particularly its origins in post-World War II Japan, and how it evolved from practices used in supermarkets to optimize manufacturing at Toyota. Key metrics for kanban in software development are also outlined, including lead time, cycle time, work in progress, and throughput, alongside the use of cumulative flow diagrams for workflow management.
Java 17 features and implementation.pptx
Java 17 features and implementation.pptxKnoldus Inc. Java 17, released in September 2021, is a long-term support (LTS) version that includes significant features such as pattern matching for switch, sealed classes, and improved memory management. This version enhances performance, security, and developer productivity, while promoting modern coding practices and compatibility with new libraries. Upgrading to Java 17 is crucial for developers to ensure stability and leverage its numerous enhancements for efficient application development.
Chaos Mesh Introducing Chaos in Kubernetes
Chaos Mesh Introducing Chaos in KubernetesKnoldus Inc. The document discusses chaos engineering, a method to find system weaknesses before they become major outages by simulating failures. It introduces Chaos Mesh, an open-source platform tailored for chaos experiments in Kubernetes, highlighting its features and various types of chaos experiments, such as pod and network failures. Key principles of chaos engineering include defining normal operation, hypothesizing impacts, introducing realistic faults, and observing system behavior.
GraalVM - A Step Ahead of JVM Presentation
GraalVM - A Step Ahead of JVM PresentationKnoldus Inc. The document presents an overview of GraalVM, highlighting its high-performance capabilities, resource efficiency, and polyglot support. It contrasts JIT (Just-In-Time) and AOT (Ahead-Of-Time) compilation methods, detailing the advantages and disadvantages of each. The content also includes etiquette for session participation, a demo outline, and performance benchmarks for applications compiled using GraalVM.
Nomad by HashiCorp Presentation (DevOps)
Nomad by HashiCorp Presentation (DevOps)Knoldus Inc. The document discusses HashiCorp's Nomad, a workload orchestrator designed for managing and deploying applications across various infrastructures. It highlights Nomad's simplicity, scalability, multi-datacenter support, and security features, comparing it with Kubernetes in terms of ease of use and workload support. Additionally, it outlines prerequisites for installation, architecture details, and key functionalities such as service discovery and job management.
Nomad by HashiCorp Presentation (DevOps)
Nomad by HashiCorp Presentation (DevOps)Knoldus Inc. The document outlines the etiquette and best practices for using HashiCorp's Nomad, a workload orchestrator designed to manage and deploy applications across infrastructures. It compares Nomad's simplicity and flexibility to Kubernetes, highlighting its capabilities in running various workloads, ease of integration with other HashiCorp tools, and robust architecture. Key features include multi-region and multi-datacenter support, scalability, security mechanisms, and the necessity of understanding specific prerequisites for implementation.
DAPR - Distributed Application Runtime Presentation
DAPR - Distributed Application Runtime PresentationKnoldus Inc. Dapr (Distributed Application Runtime) is a portable runtime designed to simplify the development of resilient microservices across cloud and edge environments, providing open APIs for various functionalities. Its core building blocks allow for extensibility and address common challenges in microservice architectures while ensuring observability and security through features such as end-to-end encryption and automated tracing. The document covers Dapr's configurations, resiliency management, and various use cases, including orchestration, edge computing, and integration with legacy systems.
Introduction to Azure Virtual WAN Presentation
Introduction to Azure Virtual WAN PresentationKnoldus Inc. Azure Virtual WAN is a comprehensive networking service that simplifies enterprise connectivity through a hub-and-spoke architecture, enhancing security and performance. It provides two main offerings, Basic and Standard, catering to different organizational needs, with features like VPN connectivity, advanced routing, and centralized management. This solution also facilitates optimal application performance and supports various use cases such as branch office connectivity and hybrid cloud integration.
Introduction to Argo Rollouts Presentation
Introduction to Argo Rollouts PresentationKnoldus Inc. This document discusses Argo Rollouts, an advanced open-source Kubernetes controller that enhances deployment strategies such as canary and blue-green deployments. It emphasizes best practices for using these deployments, including feedback submission, punctuality, and minimizing disturbances during sessions. The benefits of Argo Rollouts include reduced deployment risk, incremental updates, and automated rollbacks.
Intro to Azure Container App Presentation
Intro to Azure Container App PresentationKnoldus Inc. The document introduces Azure Container Apps, a serverless platform that supports deploying containerized applications while reducing infrastructure management. It outlines key features, benefits of microservices architecture, and compares Azure Container Apps with Kubernetes (AKS), emphasizing its user-friendly nature. Additionally, it discusses the functionality of the service, including scaling and integrations with open-source technologies like KEDA and Dapr.
Insights Unveiled Test Reporting and Observability Excellence
Insights Unveiled Test Reporting and Observability ExcellenceKnoldus Inc. The document discusses the significance of test reporting and observability in software development, detailing best practices, types of reports, and essential tools for effective implementation. It emphasizes the integration of test reporting with observability to provide a comprehensive understanding of system performance, address challenges, and improve collaboration among teams. Future trends highlight the role of AI, real-time reporting, and user-centric approaches in advancing these practices.
Introduction to Splunk Presentation (DevOps)
Introduction to Splunk Presentation (DevOps)Knoldus Inc. The document is an introduction to Splunk, detailing its features, functionality, and use cases. It emphasizes the importance of etiquette during sessions, such as punctuality and providing feedback. Splunk is a software platform that converts machine-generated data into actionable insights for businesses through data collection, analysis, and visualization.
Code Camp - Data Profiling and Quality Analysis Framework
Code Camp - Data Profiling and Quality Analysis FrameworkKnoldus Inc. The document outlines a data profiling and quality analysis framework aimed at enhancing data quality for effective test automation. It discusses the benefits, key components, and challenges of data profiling, as well as the role of AI and machine learning in improving data quality. Strategies for data quality improvement and best practices for effective data profiling and analysis are also provided to ensure accurate and reliable data management.
AWS: Messaging Services in AWS Presentation
AWS: Messaging Services in AWS PresentationKnoldus Inc. The document provides an overview of AWS messaging services, specifically Amazon SNS and SQS, highlighting their importance in distributed systems for decoupling components, scalability, reliability, and event-driven architecture. SNS is described as a push notification service for sending messages to multiple endpoints, while SQS is a pull-based message queuing service focusing on message persistence and processing. Key differences between SNS and SQS, along with use cases, polling methods, and pricing structures for both services, are also discussed.
Amazon Cognito: A Primer on Authentication and Authorization
Amazon Cognito: A Primer on Authentication and AuthorizationKnoldus Inc. The document outlines the Amazon Cognito service, which provides user identity management and data synchronization for mobile and web applications. It explains the features of user pools and identity pools, the benefits of using Amazon Cognito, and options for passwordless authentication. Additionally, it emphasizes the importance of etiquette during sessions, such as punctuality and submitting feedback.
ZIO Http A Functional Approach to Scalable and Type-Safe Web Development
ZIO Http A Functional Approach to Scalable and Type-Safe Web DevelopmentKnoldus Inc. The document outlines the etiquettes for attending a ZIO HTTP session led by Niloy Choudhury, emphasizing punctuality, providing feedback, and maintaining silence. It explains key concepts of ZIO HTTP, including routing, request handling, middleware, and server configuration, highlighting its type-safe approach for building robust HTTP servers. Overall, ZIO HTTP enables easy understanding, maintenance, and testing of server code.
Managing State & HTTP Requests In Ionic.
Managing State & HTTP Requests In Ionic.Knoldus Inc. The document discusses effective state management and HTTP requests in Ionic applications, emphasizing the importance of using RxJS subjects for centralized state handling. It outlines best practices for managing state, making HTTP requests, and ensuring secure and responsive applications. Additionally, it highlights the significance of error handling, performance optimization, and user experience improvements throughout the development process.
Ad
Recently uploaded (20)
Lessons Learned from Developing Secure AI Workflows.pdf
Lessons Learned from Developing Secure AI Workflows.pdfPriyanka Aash Lessons Learned from Developing Secure AI Workflows
Curietech AI in action - Accelerate MuleSoft development
Curietech AI in action - Accelerate MuleSoft developmentshyamraj55 CurieTech AI in Action – Accelerate MuleSoft Development
Overview:
This presentation demonstrates how CurieTech AI’s purpose-built agents empower MuleSoft developers to create integration workflows faster, more accurately, and with less manual effort
linkedin.com
+12
curietech.ai
+12
meetups.mulesoft.com
+12
.
Key Highlights:
Dedicated AI agents for every stage: Coding, Testing (MUnit), Documentation, Code Review, and Migration
curietech.ai
+7
curietech.ai
+7
medium.com
+7
DataWeave automation: Generate mappings from tables or samples—95%+ complete within minutes
linkedin.com
+7
curietech.ai
+7
medium.com
+7
Integration flow generation: Auto-create Mule flows based on specifications—speeds up boilerplate development
curietech.ai
+1
medium.com
+1
Efficient code reviews: Gain intelligent feedback on flows, patterns, and error handling
youtube.com
+8
curietech.ai
+8
curietech.ai
+8
Test & documentation automation: Auto-generate MUnit test cases, sample data, and detailed docs from code
curietech.ai
+5
curietech.ai
+5
medium.com
+5
Why Now?
Achieve 10× productivity gains, slashing development time from hours to minutes
curietech.ai
+3
curietech.ai
+3
medium.com
+3
Maintain high accuracy with code quality matching or exceeding manual efforts
curietech.ai
+2
curietech.ai
+2
curietech.ai
+2
Ideal for developers, architects, and teams wanting to scale MuleSoft projects with AI efficiency
Conclusion:
CurieTech AI transforms MuleSoft development into an AI-accelerated workflow—letting you focus on innovation, not repetition.
10 Key Challenges for AI within the EU Data Protection Framework.pdf
10 Key Challenges for AI within the EU Data Protection Framework.pdfPriyanka Aash 10 Key Challenges for AI within the EU Data Protection Framework
PyCon SG 25 - Firecracker Made Easy with Python.pdf
PyCon SG 25 - Firecracker Made Easy with Python.pdfMuhammad Yuga Nugraha Explore the ease of managing Firecracker microVM with the firecracker-python. In this session, I will introduce the basics of Firecracker microVM and demonstrate how this custom SDK facilitates microVM operations easily. We will delve into the design and development process behind the SDK, providing a behind-the-scenes look at its creation and features. While traditional Firecracker SDKs were primarily available in Go, this module brings a simplicity of Python to the table.
OpenACC and Open Hackathons Monthly Highlights June 2025
OpenACC and Open Hackathons Monthly Highlights June 2025OpenACC The OpenACC organization focuses on enhancing parallel computing skills and advancing interoperability in scientific applications through hackathons and training. The upcoming 2025 Open Accelerated Computing Summit (OACS) aims to explore the convergence of AI and HPC in scientific computing and foster knowledge sharing. This year's OACS welcomes talk submissions from a variety of topics, from Using Standard Language Parallelism to Computer Vision Applications. The document also highlights several open hackathons, a call to apply for NVIDIA Academic Grant Program and resources for optimizing scientific applications using OpenACC directives.
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdf
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdfPriyanka Aash GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now
EIS-Webinar-Engineering-Retail-Infrastructure-06-16-2025.pdf
EIS-Webinar-Engineering-Retail-Infrastructure-06-16-2025.pdfEarley Information Science As AI reshapes expectations in retail and B2B commerce, organizations are recognizing a critical reality: meaningful AI outcomes depend on well-structured, adaptable infrastructure. In this session, Seth Earley is joined by Phil Ryan - AI strategist, search technologist, and founder of Glass Leopard Technologies - for a candid conversation on what it truly means to engineer systems for scale, agility, and intelligence.
Phil draws on more than two decades of experience leading search and AI initiatives for enterprise organizations. Together, he and Seth explore the challenges businesses face when legacy architectures limit personalization, agility, and real-time decisioning - and what needs to change to support agentic technologies and next-best-action capabilities.
Key themes from the webinar include:
Composability as a prerequisite for AI - Why modular, loosely coupled systems are essential for adapting to rapid innovation and evolving business needs
Search and relevance as foundational to AI - How techniques honed-in enterprise search have laid the groundwork for more responsive and intelligent customer experiences
From MDM and CDP to agentic systems - How data platforms are evolving to support richer customer context and dynamic orchestration
Engineering for business alignment - Why successful AI programs require architectural decisions grounded in measurable outcomes
The conversation is practical and forward-looking, connecting deep technical understanding with real-world business needs. Whether you’re modernizing your commerce stack or exploring how AI can enhance product discovery, personalization, or customer journeys, this session provides a clear-eyed view of the capabilities, constraints, and priorities that matter most.
Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...
Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...All Things Open Presented at All Things Open RTP Meetup
Presented by William Hill - Developer Advocate, NVIDIA
Title: Agentic AI for Developers and Data Scientists
Build an AI Agent in 10 Lines of Code and the Concepts Behind the Code
Abstract: In this talk we will demonstrate building a working data science AI agent in 10 lines of basic Python code in a Colab notebook. Our AI Agent will perform LLM prompt-driven visual analysis using open-source libraries. In this session we will show how to develop an AI Agent using GPUs through NVIDIA’s developer program and Google Colab notebooks. After coding our AI Agent, we will break down the 10 lines of code. We will show the key components and open source library integrations that enable the agent's functionality, focusing on practical implementation details and then the theoretical concepts. The presentation concludes with a survey of current LLM technologies and the latest trends in developing AI applications for enthusiasts and enterprises
Security Tips for Enterprise Azure Solutions
Security Tips for Enterprise Azure SolutionsMichele Leroux Bustamante Delivering solutions to Azure may involve a variety of architecture patterns involving your applications, APIs data and associated Azure resources that comprise the solution. This session will use reference architectures to illustrate the security considerations to protect your Azure resources and data, how to achieve Zero Trust, and why it matters. Topics covered will include specific security recommendations for types Azure resources and related network security practices. The goal is to give you a breadth of understanding as to typical security requirements to meet compliance and security controls in an enterprise solution.
The Growing Value and Application of FME & GenAI
The Growing Value and Application of FME & GenAISafe Software With the cost of using Generative AI services dropping exponentially and the array of available models continually expanding, integrating AI into FME workflows has become inexpensive, accessible and effective. This presentation explores how GenAI within FME can cost-effectively transform data workflows by automating data extraction, validation, classification and augmentation tasks. We’ll discuss how FME’s no-code flexibility enables users to combine Generative AI and Computer Vision tools that create efficient workflows tailored to specific challenges. Using recent practical examples, we’ll demonstrate how these integrations can simplify complex tasks, save time and enhance data quality.
AI vs Human Writing: Can You Tell the Difference?
AI vs Human Writing: Can You Tell the Difference?Shashi Sathyanarayana, Ph.D This slide illustrates a side-by-side comparison between human-written, AI-written, and ambiguous content. It highlights subtle cues that help readers assess authenticity, raising essential questions about the future of communication, trust, and thought leadership in the age of generative AI.
Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...
Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...Josef Weingand IBM LTO10
"How to survive Black Friday: preparing e-commerce for a peak season", Yurii ...
"How to survive Black Friday: preparing e-commerce for a peak season", Yurii ...Fwdays We will explore how e-commerce projects prepare for the busiest time of the year, which key aspects to focus on, and what to expect. We’ll share our experience in setting up auto-scaling, load balancing, and discuss the loads that Silpo handles, as well as the solutions that help us navigate this season without failures.
AI Agents and FME: A How-to Guide on Generating Synthetic Metadata
AI Agents and FME: A How-to Guide on Generating Synthetic MetadataSafe Software In the world of AI agents, semantics is king. Good metadata is thus essential in an organization's AI readiness checklist. But how do we keep up with the massive influx of new data? In this talk we go over the tips and tricks in generating synthetic metadata for the consumption of human users and AI agents alike.
Mastering AI Workflows with FME by Mark Döring
Mastering AI Workflows with FME by Mark DöringSafe Software Harness the full potential of AI with FME: From creating high-quality training data to optimizing models and utilizing results, FME supports every step of your AI workflow. Seamlessly integrate a wide range of models, including those for data enhancement, forecasting, image and object recognition, and large language models. Customize AI models to meet your exact needs with FME’s powerful tools for training, optimization, and seamless integration
cnc-processing-centers-centateq-p-110-en.pdf
cnc-processing-centers-centateq-p-110-en.pdfAmirStern2 מרכז עיבודים תעשייתי בעל 3/4/5 צירים, עד 22 החלפות כלים עם כל אפשרויות העיבוד הדרושות. בעל שטח עבודה גדול ומחשב נוח וקל להפעלה בשפה העברית/רוסית/אנגלית/ספרדית/ערבית ועוד..
מסוגל לבצע פעולות עיבוד שונות המתאימות לענפים שונים: קידוח אנכי, אופקי, ניסור, וכרסום אנכי.
Raman Bhaumik - Passionate Tech Enthusiast
Raman Bhaumik - Passionate Tech EnthusiastRaman Bhaumik A Junior Software Developer with a flair for innovation, Raman Bhaumik excels in delivering scalable web solutions. With three years of experience and a solid foundation in Java, Python, JavaScript, and SQL, she has streamlined task tracking by 20% and improved application stability.
2025_06_18 - OpenMetadata Community Meeting.pdf
2025_06_18 - OpenMetadata Community Meeting.pdfOpenMetadata The community meetup was held Wednesday June 18, 2025 @ 9:00 AM PST.
Catch the next OpenMetadata Community Meetup @ https://www.meetup.com/openmetadata-meetup-group/
In this month's OpenMetadata Community Meetup, "Enforcing Quality & SLAs with OpenMetadata Data Contracts," we covered data contracts, why they matter, and how to implement them in OpenMetadata to increase the quality of your data assets!
Agenda Highlights:
👋 Introducing Data Contracts: An agreement between data producers and consumers
📝 Data Contracts key components: Understanding a contract and its purpose
🧑🎨 Writing your first contract: How to create your own contracts in OpenMetadata
🦾 An OpenMetadata MCP Server update!
➕ And More!
Stateful stream processing with Apache Flink
- 1. Presented By: Kundan Kumar Software Consultant Stateful Stream Processing with Apache Flink
- 2. Lack of etiquette and manners is a huge turn off. KnolX Etiquettes Punctuality Respect Knolx session timings, you are requested not to join sessions after a 5 minutes threshold post the session start time. Feedback Make sure to submit a constructive feedback for all sessions as it is very helpful for the presenter. Mute Be on mute until you have questions or concerns.
- 3. Agenda 01 What is stateful stream processing 02 Flink takes on stateful stream processing Demo 03
- 4. What is Stateful Stream Processing? Streaming and Stream Processing: Stream processing is the processing of data in motion, or in other words, computing on data directly as it is produced or received. The systems that receive and send the data streams and execute the application or analytics logic are called stream processors.
- 5. Stateful Stream Processing: Stateful stream processing is a subset of stream processing in which the computation maintains contextual state. This state is used to store information derived from the previously-seen events. Stateful stream processing means a “State” is shared between events(stream entities). And therefore past events can influence the way the current events are processed.
- 6. Flink takes on stateful stream processing Flink in nutshell- ● Apache Flink is a Big Data framework and distributed processing engine for stateful computations over unbounded and bounded data streams.
- 7. ➢ A Flink application may consume real-time data from streaming sources such as message queues or distributed logs, like Apache Kafka or Kinesis. ➢ Flink can also consume bounded, historic data from a variety of data sources. ➢ The streams of results being produced by a Flink application can be sent to a wide variety of systems that can be connected as sinks. ➢ Fast, In memory, scalable, large state, fault tolerant, event time, exactly once.
- 9. ➢ Programs in Flink are inherently parallel and distributed. ➢ During execution, a stream has one or more stream partitions, and each operator has one or more operator subtasks.
- 10. ➢ Flink facilitate stateful operations. ➢ Current handling event can depend on the accumulated effect of all the events that came before it. ➢ The set of parallel instances of a stateful operator is effectively a sharded key-value store. Each parallel instance is responsible for handling events for a specific group of keys, and the state for those keys is kept locally.
- 12. States in Flink ➢ Operator State: State is maintained on per operator basis on stream. Special type of state used in source and sink implementations. ➢ Keyed State: Maintaining state on per key basis on stream. Stores state associated with the same key. Embedded key value store. ➢ Broadcast State: Special type of operator state used where records of one stream will be broadcast to all downstream task which needs access to those records. ➢ Queryable State: Feature that allow client API’s to query Jobstate from outside Flink.
- 13. Stateful streaming application in Flink
- 15. State Backends 1. Memory state backend: ➢ This is the default backend used by Flink in case nothing is configured. ➢ Persists the data in the memory of each task manager’s Heap. ➢ This state should never be used in production jobs. ➢ The state creates a backup of the data (also known as checkpointing) in the job manager memory which puts unnecessary pressure on the job manager's operational stability.
- 16. 2. File System Backend ➢ This backend is similar to Memory state backend except, it stores the backup on the filesystem rather than job manager’s memory. ➢ The filesystem can be task manager's local filesystem or a durable store such as HDFS/S3. 3. RocksDB backend ➢ This backend uses RocksDB by Facebook to store the data ➢ RocksDB maintains an in-memory table (also known as mem-table) along with bloom filters, reading recent data also is extremely fast. ➢ Each task manager maintains its own Rocks DB file and the backup of this state is checkpointed to a durable store such as HDFS/S3. ➢ This is the only backend which offers support for incremental checkpointing i.e. taking a backup of only modified data rather than complete data.
- 17. Checkpointing Checkpoint: Specific marked point in each input stream from which stream can replayed. Flink implements it by persisting state of all stateful operator. Periodically save state to reliable storage system. Stream Barriers: Lightweight stream marker with unique ID’s. Injected by Flink into input stream and flow with stream in line.
- 20. Demo
- 21. Q/A
- 22. References 1. https://flink.apache.org 2. https://ci.apache.org/projects/flink/flink-docs-release-1.12/con cepts/stateful-stream-processing.html#unaligned-checkpointin g 3. Book: Learning Apache Flink By Tanmay Deshpande
- 23. Thank You !