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Big Data & Hadoop Market
 According to Forrester: growth rate of 13% for
the next 5 years, which is more
than twice w.r.t. predicted general IT growth
 U.S. and International Operations (29%) and
Enterprises (27%) lead the adoption of Big
Data globally
 Asia Pacific to be fastest growing Hadoop
market with a CAGR of 59.2 %
 Companies focusing on improving customer
relationships (55%) and making the business
more data-focused (53%)
2013 2014 2015 2016
Hadoop Market
CAGR of 58.2 %

Hadoop Job Trends

Agenda for Today
Hadoop Interview Questions
 Big Data & Hadoop
 HDFS
 MapReduce
 Apache Hive
 Apache Pig
 Apache HBase and Sqoop

Big Data & Hadoop Interview Questions
“The harder I practice, the luckier I get.”
Gary Player

Big Data & Hadoop
Q. What are the five V’s associated with Big Data?

Big Data & Hadoop
Q. What are the five V’s associated with Big Data?
Big
Data

Big Data & Hadoop
Q. Differentiate between structured, semi-structured and unstructured data?

Big Data & Hadoop
 Structured  Semi - Structured  Unstructured
 Organized data format
 Data schema is fixed
 Example:
RDBMS data, etc.
 Partial organized data
 Lacks formal structure
of a data model
 Example:
XML & JSON files, etc.
 Un-organized data
 Unknown schema
 Example:
multi - media files, etc.
Q. Differentiate between structured, semi-structured and unstructured data?

Big Data & Hadoop
Q. How Hadoop differs from Traditional Processing System using RDBMS?

Big Data & Hadoop
Q. How Hadoop differs from Traditional Processing System using RDBMS?
RDBMS Hadoop
RDBMS relies on the structured data and the schema of
the data is always known.
Any kind of data can be stored into Hadoop i.e. Be it
structured, unstructured or semi-structured.
RDBMS provides limited or no processing capabilities. Hadoop allows us to process the data in distributed
parallel fashion.
RDBMS is based on ‘schema on write’ where schema
validation is done before loading the data.
On the contrary, Hadoop follows the schema on read
policy.
In RDBMS, reads are fast because the schema of the data
is already known.
The writes are fast in HDFS because no schema validation
happens during HDFS write.
Suitable for OLTP (Online Transaction Processing) Suitable for OLAP (Online Analytical Processing)
Licensed software Hadoop is an open source framework.

Big Data & Hadoop
Q. Explain the components of Hadoop and their services.

Big Data & Hadoop
Q. What are the main Hadoop configuration files?

Big Data & Hadoop
Q. What are the main Hadoop configuration files?
hadoop-env.sh core-site.xml
hdfs-site.xml yarn-site.xml
mapred-site.xml masters
slaves

HDFS Interview Questions
“A person who never made a mistake never tried
anything new.”
Albert Einstein

HDFS
Q. HDFS stores data using commodity hardware which has higher chances of failures. So, How HDFS
ensures the fault tolerance capability of the system?

HDFS
Q. HDFS stores data using commodity hardware which has higher chances of failures. So, How HDFS
ensures the fault tolerance capability of the system?
 HDFS replicates the blocks and
stores on different DataNodes
 Default Replication Factor is set
to 3

HDFS
Q. What is the problem in having lots of small files in HDFS? Provide one method to overcome this
problem.

HDFS
Q. What is the problem in having lots of small files in HDFS? Provide one method to overcome this
problem.
> hadoop archive –archiveName edureka_archive.har /input/location /output/location
Problem:
 Too Many Small Files = Too Many Blocks
 Too Many Blocks == Too Many Metadata
 Managing this huge number of metadata is
difficult
 Increase in cost of seek
Solution:
 Hadoop Archive
 It clubs small HDFS files into a single archive
HDFS Files
(small)
.HAR file

HDFS
Q. Suppose there is file of size 514 MB stored in HDFS (Hadoop 2.x) using default block size
configuration and default replication factor. Then, how many blocks will be created in total and what
will be the size of each block?

HDFS
Q. Suppose there is file of size 514 MB stored in HDFS (Hadoop 2.x) using default block size
configuration and default replication factor. Then, how many blocks will be created in total and what
will be the size of each block?
 Default Block Size = 128 MB
 514 MB / 128 MB = 4.05 == 5 Blocks
 Replication Factor = 3
 Total Blocks = 5 * 3 = 15
 Total size = 514 * 3 = 1542 MB

HDFS
Q. How to copy a file into HDFS with a different block size to that of existing block size configuration?

HDFS
Q. How to copy a file into HDFS with a different block size to that of existing block size configuration?
 Block size: 32 MB = 33554432 Bytes ( Default block size: 128 MB)
 Command:
hadoop fs -Ddfs.blocksize=33554432 -copyFromLocal /local/test.txt /sample_hdfs
 Check the block size of test.txt
hadoop fs -stat %o /sample_hdfs/test.txt
HDFS
Files
(existing)
128
MB
128
MB
test.txt
(local)
-Ddfs.blocksize=33554432
test.txt
(HDFS)
32
MB
32
MB
move to HDFS: /sample_hdfs
HDFS HDFS

HDFS
Q. What is a block scanner in HDFS?

HDFS
Q. What is a block scanner in HDFS?
 Block scanner maintains integrity of the data blocks
 It runs periodically on every DataNode to verify whether
the data blocks stored are correct or not
Steps:
1. DataNode reports to NameNode
2. NameNode schedules the creation of new
replicas using the good replicas
3. Once replication factor (uncorrupted replicas)
reaches to the required level, deletion of
corrupted blocks takes place
Note: This question is generally asked for the position
Hadoop Admin

HDFS
Q. Can multiple clients write into an HDFS file concurrently?

HDFS
Q. Can multiple clients write into an HDFS file concurrently?
 HDFS follows Single Writer Multiple Reader Model
 The client which opens a file for writing is granted a lease
by the NameNode
 NameNode rejects write request of other clients for the
file which is currently being written by someone else
HDFS
ReadWrite

HDFS
Q. What do you mean by the High Availability of a NameNode? How is it achieved?

HDFS
Q. What do you mean by the High Availability of a NameNode? How is it achieved?
 NameNode used to be Single Point of Failure in
Hadoop 1.x
 High Availability refers to the condition where a
NameNode must remain active throughout the cluster
 HDFS HA Architecture in Hadoop 2.x allows us to
have two NameNode in an Active/Passive
configuration.

MapReduce Interview Questions
“Never tell me the sky’s the limit when there are
footprints on the moon.”
–Author Unknown

MapReduce
Q. Explain the process of spilling in MapReduce?

MapReduce
Q. Explain the process of spilling in MapReduce?
Local Disc
 The output of a map task is written into a circular
memory buffer (RAM).
 Default Buffer size is set to 100 MB as specified in
mapreduce.task.io.sort.mb
 Spilling is a process of copying the data from memory
buffer to disc after a certain threshold is reached
 Default spilling threshold is 0.8 as specified in
mapreduce.map.sort.spill.percent
20 %
50 %80%80%
Spill data
Node Manager
RAM

MapReduce
Q. What is the difference between blocks, input splits and records?

MapReduce
Q. What is the difference between blocks, input splits and records?
Blocks
Input Splits
Records
Physical Division
Logical Division
 Blocks: Data in HDFS is physically
stored as blocks
 Input Splits: Logical chunks of data to
be processed by an individual mapper
 Records: Each input split is comprised
of records e.g. in a text file each line is
a record

MapReduce
Q. What is the role of RecordReader in Hadoop MapReduce?

MapReduce
Q. What is the role of RecordReader in Hadoop MapReduce?
 RecordReader converts the data present in a file into (key, value) pairs suitable for reading by the
Mapper task
 The RecordReader instance is defined by the Input Format
1 David
2 Cassie
3 Remo
4 Ramesh
…
RecordReader
Key Value
0 1 David
57 2 Cassie
122 3 Remo
171 4 Ramesh
…
Mapper

MapReduce
Q. What is the significance of counters in MapReduce?

1 David
2%^&%d
3 Jeff
4 Shawn
5$*&!#$
MapReduce
Q. What is the significance of counters in MapReduce?
 Used for gathering statistics about the job:
 for quality control
 for application-level statistics
 Easier to retrieve counters as compared to log messages for large distributed job
 For example: Counting the number of invalid records, etc.
MapReduce Output
Counter: 02
+1
1
invalid records

MapReduce
Q. Why the output of map tasks are stored ( spilled ) into local disc and not in HDFS?

MapReduce
Q. Why the output of map tasks are stored ( spilled ) into local disc and not in HDFS?
 The outputs of map task are the intermediate key-value
pairs which is then processed by reducer
 Intermediate output is not required after completion of
job
 Storing these intermediate output into HDFS and
replicating it will create unnecessary overhead.
Local Disc
Mapper Reducer
NodeManager
HDFS
output

MapReduce
Q. Define Speculative Execution

MapReduce
Q. Define Speculative Execution
 If a task is detected to be running slower, an equivalent
task is launched so as to maintain the critical path of the
job
 Scheduler tracks the progress of all the tasks (map and
reduce) and launches speculative duplicates for slower
tasks
 After completion of a task, all running duplicates task are
killed
MRTask
(slow)
Node Manager
MRTask
(duplicate)
Node Manager
Scheduler
slow
task
progress
launch
speculative

MapReduce
Q. How will you prevent a file from splitting in case you want the whole file to be processed by the
same mapper?

MapReduce
Q. How will you prevent a file from splitting in case you want the whole file to be processed by the
same mapper?
Method 1: Increase the minimum split size to be larger than the largest file inside the driver section
i. conf.set ("mapred.min.split.size", “size_larger_than_file_size");
ii. Input Split Computation Formula - max ( minimumSize, min ( maximumSize, blockSize ) )
public class NonSplittableTextInputFormat extends TextInputFormat {
@Override
protected boolean isSplitable (JobContext context, Path file) {
return false;
}
}
Method 2: Modify the InputFormat class that you want to use:
i. Subclass the concrete subclass of FileInputFormat and override the isSplitable() method to return
false as shown below:

MapReduce
Q. Is it legal to set the number of reducer task to zero? Where the output will be stored in this case?

MapReduce
Q. Is it legal to set the number of reducer task to zero? Where the output will be stored in this case?
 Legal to set the number of reducer task to zero
 It is done when there is no need for a reducer like in the
cases where inputs needs to be transformed into a
particular format, map side join etc.
 Map outputs is directly stored into the HDFS as specified
by the client
HDFS
(Input)
Map Reduce
HDFS
(Output)
HDFS
(Input)
Map Reduce
HDFS
(Output)
Reducer set to zero

MapReduce
Q. What is the role of Application Master in a MapReduce Job?

MapReduce
Q. What is the role of Application Master in a MapReduce Job?
 Acts as a helper process for ResourceManager
 Initializes the job and track of the job’s progress
 Retrieves the input splits computed by the client
 Negotiates the resources needed for running a job with
the ResourceManager
 Creates a map task object for each split
Client RM NM AM
submit job
launch AM
ask for resources
run task
status
unregister

MapReduce
Q. What do you mean by MapReduce task running in uber mode?

MapReduce
Q. What do you mean by MapReduce task running in uber mode?
 If a job is small, ApplicationMaster chooses to run the tasks in its own JVM and are called
uber task
 It reduces the overhead of allocating new containers for running the tasks
 A MapReduce job is decided as uber task if:
 It requires less than 10 mappers
 It requires only one reducer
 The input size is less than the HDFS block size
 Parameters to be set for deciding uber task:
 mapreduce.job.ubertask.maxmaps
 mapreduce.job.ubertask.maxreduces
 mapreduce.job.ubertask.maxbytes
 To enable uber task: mapreduce.job.ubertask.enable to true.

MapReduce
Client Node
JVM
MR
Code
MR
Job
run job
Node Manager
RM Node
Node Manager
MR
Task
(uber)
AppMaster JVM
ResourceManager
JVM
HDFS
1. Submit Job
2. Launch
AppMaster
3. output
Copy job
resources
Criteria:
 It requires less than 10 mappers
 It requires only one reducer
 The input size is less than the HDFS
block size

MapReduce
Q. How will you enhance the performance of MapReduce job when dealing with
too many small files?

MapReduce
Q. How will you enhance the performance of MapReduce job when dealing with
too many small files?
 CombineFileInputFormat can be used to solve this
problem
 CombineFileInputFormat packs all the small files
into input splits where each split is processed by a
single mapper
 Takes node and rack locality into account when
deciding which blocks to place in the same split
 Can process the input files efficiently in a typical
MapReduce job

Apache Hive Interview Questions
“Generally, the question that seems to be
complicated have simple answers.”
– Anonymous

Apache Hive
Q. Where does the data of a Hive table gets stored?
Q. Why HDFS is not used by the Hive metastore for storage?

Apache Hive
 By default, the Hive table is stored in an HDFS directory: /user/hive/warehouse
 It is specified in hive.metastore.warehouse.dir configuration parameter present in the hive-site.xml

Apache Hive
 By default, the Hive table is stored in an HDFS directory: /user/hive/warehouse
 It is specified in hive.metastore.warehouse.dir configuration parameter present in the hive-site.xml
 Editing files or data present in HDFS is not allowed.
 Metastore stores metadata using RDBMS to provide low query latency
 HDFS read/write operations are time consuming processes

Apache Hive
Scenario:
Suppose, I have installed Apache Hive on top of my Hadoop cluster using default metastore configuration.
Then, what will happen if we have multiple clients trying to access Hive at the same time?

Apache Hive
Scenario:
Suppose, I have installed Apache Hive on top of my Hadoop cluster using default metastore configuration.
Then, what will happen if we have multiple clients trying to access Hive at the same time?
 Multiple client access is not allowed in default metastore configuration or embedded mode
 One may use following two metastore configurations:
1. Local Metastore Configuration 2. Remote Metastore Configuration

Apache Hive
Q. What is the difference between external table and managed table?

Apache Hive
Q. What is the difference between external table and managed table?
Managed Table:
 Hive responsible for managing the table
data
 While dropping the table, Metadata
information along with the table data is
deleted from the Hive warehouse
External Table:
 Hive is responsible for managing only table
metadata not the table data
 While dropping the table, Hive just deletes the
metadata information leaving the table data
untouched

Apache Hive
Q. When should we use SORT BY instead of ORDER BY ?

Apache Hive
Q. When should we use SORT BY instead of ORDER BY ?
 SORT BY clause sorts the data using multiple reducers
Reducer OutputDataset
Reducer 1
Reducer 2
Reducer n
Output
 ORDER BY sorts all of the data together using a single
reducer
SORT BY should be used to sort huge datasets
Dataset

Apache Hive
Q. What is the difference between partition and bucket in Hive?

Apache Hive
Scenario:
CREATE TABLE transaction_details (cust_id INT, amount FLOAT, month STRING, country STRING) ROW
FORMAT DELIMITED FIELDS TERMINATED BY ‘,’ ;
Now, after inserting 50,000 tuples in this table, I want to know the total revenue generated for the month -
January. But, Hive is taking too much time in processing this query. How will you solve this problem?

 Create a partitioned table:
 CREATE TABLE partitioned_transaction (cust_id INT, amount FLOAT, country STRING) PARTITIONED BY (month
STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,’ ;
 Enable dynamic partitioning in Hive:
 SET hive.exec.dynamic.partition = true;
 SET hive.exec.dynamic.partition.mode = nonstrict;
 Transfer the data :
 INSERT OVERWRITE TABLE partitioned_transaction PARTITION (month) SELECT cust_id, amount, country,
month FROM transaction_details;
 Run the query :
 SELECT SUM(amount) FROM partitioned_transaction WHERE month= ‘January’;
Apache Hive

Q. What is dynamic partitioning and when is it used?
Apache Hive

Q. What is dynamic partitioning and when is it used?
 Values for partition columns are known during runtime
 One may use dynamic partition in following cases:
 Loading data from an existing non-partitioned table to improve the sampling (query latency)
 Values of the partitions are not known before hand and therefore, finding these unknown
partition values manually from huge data sets is a tedious task
Apache Hive

Q. How Hive distributes the rows into buckets?
Apache Hive

Q. How Hive distributes the rows into buckets?
 Bucket number is determined for a row by using the formula:
hash_function (bucketing_column) modulo (num_of_buckets)
 hash_function depends on the column data type i.e. for int type it is equal to value of column
 hash_function for other data types is complex to calculate
Id Name
1 John
2 Mike
3 Shawn
2, Mike
1, John
3, Shawn
Bucket 1
Bucket 2
 hash_function (1) = 1
hash_function (id) = id
 1 mod 2 = 1
 2 mod 2 = 0
 3 mod 2 = 1
id mod 2 = bucket num
Apache Hive

Scenario:
Suppose, I have a CSV file – ‘sample.csv’ present in ‘/temp’ directory with the following
entries:
id first_name last_name e-mail gender ip
1 Hugh Jackman hugh32@sun.co Male 136.90.241.52
2 David Lawrence dlawrence@gmail.co Male 101.177.15.130
3 Andy Hall anyhall@yahoo.co Female 114.123.153.64
4 Samuel Jackson samjackson@rediff.co Male 91.121.145.67
5 Emily Rose rosemily@edureka.co Female 117.123.108.98
How will you consume this CSV file into the Hive warehouse using built-in SerDe?
Apache Hive

 A SerDe allows us to convert the unstructured bytes into a record that we can process using Hive.
 CREATE EXTERNAL TABLE sample (id INT, first_name STRING, last_name STRING, email STRING, gender
STRING, ip_address STRING)
ROW FORMAT SERDE ‘org.apache.hadoop.hive.serde2.OpenCSVSerde’
STORED AS TEXTFILE LOCATION ‘/temp’;
 SELECT first_name FROM sample WHERE gender = ‘male’;
Note:
 Hive provides several built – in
SerDe like for JSON, TSV etc.
 Useful in cases where you
have embedded commas in
delimited fields
Apache Hive

Scenario:
 I have a lot of small CSV files present in /input directory in HDFS and I want to create a single Hive
table corresponding to these files.
 The data in these files are in the format: {id, name, e-mail, country}
Now, as we know, Hadoop performance degrades when we use lots of small files. So, how will you
solve this problem?
Apache Hive

 Create a temporary table:
CREATE TABLE temp_table (id INT, name STRING, e-mail STRING, country STRING)
ROW FORMAT FIELDS DELIMITED TERMINATED BY ‘,’ STORED AS TEXTFILE;
 Load the data from the input directory into temp_table:
LOAD DATA INPATH ‘/input’ INTO TABLE temp_table;
 Create a table that will store data in SequenceFile format:
CREATE TABLE sample_seqfile (id INT, name STRING, e-mail STRING, country STRING)
ROW FORMAT FIELDS DELIMITED TERMINATED BY ‘,’ STORED AS SEQUENCEFILE;
 Transfer the data from the temporary table into the sample_seqfile table:
INSERT OVERWRITE TABLE sample_seqfile SELECT * FROM temp_table;
Apache Hive
 When Hive converts queries to MapReduce jobs, it decides on the appropriate key-value pairs to be used
for a given record
 Sequence files are flat files consisting of binary key-value pairs
 Using sequence file, one can club two or more smaller files to make them one single sequence file

Apache Pig Interview Questions
“Whenever you are asked if you can do a job, tell
them, 'Certainly I can!' , Then get busy and find out
how to do it.”
–Theodore Roosevelt

Apache Pig
Q. What is the difference between logical and physical plans?

Apache Pig
Q. What is the difference between logical and physical plans?
Logical Plan:
 Created for each line in pig script if no syntax error is
found by interpreter
 No data processing happens during creation of logical
plan
Physical Plan:
 Physical plan is basically a series of map reduce jobs
 Describes the physical operators to execute the script,
without reference to how they will be executed in
MapReduce

Apache Pig
Q. What is a bag in Pig Latin?

Apache Pig
Q. What is a bag in Pig Latin?
 Unordered collection of tuples
 Duplicate tuples are allowed
 Tuples with differing numbers of fields is allowed
 For example:
{ (Linkin Park, 7, California),
(Metallica, 8),
(Mega Death, Los Angeles) }

Apache Pig
Q. How Apache Pig handles unstructured data which is difficult in case of Apache
Hive?

Apache Pig
Q. How Apache Pig handles unstructured data which is difficult in case of Apache
Hive?
No Datatype
{a, b, c}
$2
(positional notation)
c
(3rd field)
missing schema JOIN, COGROUP, etc NULL (schema)
schema is NULL Byte Array (default) Data type definition
(runtime)
Byte Array (default)

Apache Pig
Q. What are the different execution modes available in Pig?

Apache Pig
Q. What are the different execution modes available in Pig?
MapReduce Mode:
 Default mode
 Requires access to a Hadoop
cluster
 Input and output data are present
on HDFS
Local Mode:
 Requires access to a single machine
 ‘-x ’ flag is used to specify the local
mode environment (pig -x local)
 Input and output data are present on
local file system

Apache Pig
Q. What does Flatten do in Pig?

Apache Pig
Q. What does Flatten do in Pig?
 Flatten un-nests bags and tuples.
 For tuples, the Flatten operator will substitute the fields of a tuple in place of the tuple
 For example:
 Un-nesting bags is a little complex as it requires creating new tuples
(a, (b, c))
GENERATE $0, flatten($1)
(a, b, c)

HBase & Sqoop Interview Questions
“Take risks: if you win, you will be happy; if you
lose, you will be wise.”
–Anonymous

HBase
Q. What are the key components of HBase?

HBase
Q. What are the key components of HBase?
 HMaster manages the Region
Servers
 Region Server manages a group of
regions
 Zooeeper acts as a coordinator
inside HBase environment

HBase
Q. How do we back up a HBase cluster?

HBase
Q. How do we back up a HBase cluster?
1. Full Shutdown Backup
 Useful for cases where HBase cluster shutdown is
possible
 Steps:
• Stop HBase: Stop the HBase services first
• Distcp: Copy the contents of the HBase directory
into another HDFS directory in different or same
cluster
2. Live Cluster Backup
 Useful for live cluster that cannot afford downtime
 Steps:
• CopyTable: Copy data from one table to
another on the same or different cluster
• Export: Dumps the content of a table into
HDFS on the same cluster

HBase
Q. What is a Bloom filter and how does it help in searching rows?

HBase
Q. What is a Bloom filter and how does it help in searching rows?
 Used to improve the overall throughput of the cluster
 Space efficient mechanism to test whether a HFile contains a specific row or row-col cell
 Saves the time in scanning non - relevant blocks for a given row key

HBase
Q. What is the role of JDBC driver in a Sqoop set up?

Sqoop
Q. What is the role of JDBC driver in a Sqoop set up?
 To connect to different relational databases Sqoop needs a connector
 Almost every DB vendor makes this connecter available as a JDBC driver which is specific to
that DB
 Sqoop needs the JDBC driver of each of the database that it needs to interact with

Q. When to use --target-dir and when to use --warehouse-dir while importing data?
Sqoop

Q. When to use --target-dir and when to use --warehouse-dir while importing data?
 --target-dir is used for specifying a particular directory in HDFS
 --warehouse-dir is used for specifying the parent directory of all the Sqoop jobs
 In the later case, Sqoop will create directory with the same name as that of table under the
parent directory
Sqoop

Q. What does the following query do:
$ sqoop import --connect jdbc:mysql://host/dbname --table EMPLOYEES --where
"start_date” > '2012-11-09‘
Sqoop

It imports the employees who have joined after 9-Nov-2012
Sqoop
Q. What does the following query do:
$ sqoop import --connect jdbc:mysql://host/dbname --table EMPLOYEES --where
"start_date” > '2012-11-09‘

Scenario:
In a Sqoop import command you have mentioned to run 8 parallel MapReduce tasks but
Sqoop runs only 4
What can be the reason?
Sqoop

Scenario:
In a Sqoop import command you have mentioned to run 8 parallel MapReduce tasks but
Sqoop runs only 4
What can be the reason?
In this case, the MapReduce cluster is configured to run 4 parallel tasks. Therefore, the
Sqoop command must have the number of parallel tasks less or equal to that of the
MapReduce cluster
Sqoop

Q. Give a Sqoop command to show all the databases in a MySQL server.
Sqoop

Q. Give a Sqoop command to show all the databases in a MySQL server.
 Issue the command given below:
$ sqoop list-databases --connect jdbc:mysql://database.example.com/
Sqoop

Learning Resources
 Top 50 Hadoop Interview Questions:
www.edureka.co/blog/interview-questions/top-50-hadoop-interview-questions-2016
 HDFS Interview Questions:
www.edureka.co/blog/interview-questions/hadoop-interview-questions-hdfs-2
 MapReduce Interview Questions:
www.edureka.co/blog/interview-questions/hadoop-interview-questions-mapreduce
 Apache Hive Interview Questions:
www.edureka.co/blog/interview-questions/hive-interview-questions
 Apache Pig Interview Questions:
www.edureka.co/blog/interview-questions/hadoop-interview-questions-pig
 Apache HBase Interview Questions:
www.edureka.co/blog/interview-questions/hbase-interview-questions

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