Tutorials Archives | Page 10 of 30 | 800+ Java & Big Data Interview Q&As

Blog Archives

« 1 … 6 7 8 9 10 11 12 13 14 … 30 »

04: Convert XML file To an Avro File with Apache Spark – writing & reading

This extends Convert XML file To an Avro File – writing & reading. Step 1: The pom.xml file should include the Apache Spark & Avro libraries as shown below.


<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>my.app</groupId>
    <artifactId>datawrangling-java</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    <packaging>jar</packaging>

    <name>datawrangling-java</name>
    <url>http://maven.apache.org</url>

    <properties>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <junit.version>4.8.1</junit.version>
        <avro.version>1.7.7</avro.version>
        <hadoop.version>2.7.2</hadoop.version>
        <spark-version>1.3.0</spark-version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>${junit.version}</version>
            <scope>test</scope>
        </dependency>

        <!-- AVRO -->
        <dependency>
            <groupId>org.apache.avro</groupId>
            <artifactId>avro</artifactId>
            <version>${avro.version}</version>
        </dependency>
         <dependency>
            <groupId>org.apache.avro</groupId>
            <artifactId>avro-mapred</artifactId>
            <version>${avro.version}</version>
            <classifier>hadoop2</classifier>
            <exclusions>
                <exclusion>
                    <groupId>org.apache.avro</groupId>
                    <artifactId>*</artifactId>
                </exclusion>
            </exclusions>
        </dependency> 
        
        <!-- Hadoop -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>${hadoop.version}</version>
            <exclusions>
                <exclusion>
                    <groupId>javax.servlet</groupId>
                    <artifactId>*</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
            <exclusions>
                <exclusion>
                    <groupId>javax.servlet</groupId>
                    <artifactId>*</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <!-- XML -->
        <dependency>
            <groupId>org.eclipse.persistence</groupId>
            <artifactId>org.eclipse.persistence.moxy</artifactId>
            <version>2.6.2</version>
        </dependency>

        <!-- Spark -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark-version}</version>
            <exclusions>
                <exclusion>
                    <groupId>javax.servlet</groupId>
                    <artifactId>*</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
    </dependencies>
</project>

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

<artifactId>datawrangling-java</artifactId>

<version>0.0.1-SNAPSHOT</version>

<name>datawrangling-java</name>

<url>http://maven.apache.org</url>

<maven.compiler.source>1.8</maven.compiler.source>

<maven.compiler.target>1.8</maven.compiler.target>

<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>

<junit.version>4.8.1</junit.version>

<avro.version>1.7.7</avro.version>

<hadoop.version>2.7.2</hadoop.version>

<spark-version>1.3.0</spark-version>

</properties>

<groupId>junit</groupId>

<artifactId>junit</artifactId>

<version>${junit.version}</version>

</dependency>

<groupId>org.apache.avro</groupId>

<version>${avro.version}</version>

</dependency>

<groupId>org.apache.avro</groupId>

<artifactId>avro-mapred</artifactId>

<version>${avro.version}</version>

<classifier>hadoop2</classifier>

<groupId>org.apache.avro</groupId>

</exclusion>

</exclusions>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-hdfs</artifactId>

<version>${hadoop.version}</version>

<groupId>javax.servlet</groupId>

</exclusion>

</exclusions>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-client</artifactId>

<version>${hadoop.version}</version>

<groupId>javax.servlet</groupId>

</exclusion>

</exclusions>

</dependency>

<groupId>org.eclipse.persistence</groupId>

<artifactId>org.eclipse.persistence.moxy</artifactId>

</dependency>

<groupId>org.apache.spark</groupId>

<artifactId>spark-core_2.11</artifactId>

<version>${spark-version}</version>

<groupId>javax.servlet</groupId>

</exclusion>

</exclusions>

</dependency>

</dependencies>

</project>

Step 2: The report.xml file under “src/main/resources/data”. … Read more ›...

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: Create new or append to an existing AVRO file tutorial

This extends Create or append a file to HDFS – Hadoop API tutorial to write an AVRO file to HDFS. Step 1: Include the AVRO library files in the pom.xml file.


<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.mytutorial</groupId>
    <artifactId>simple-hadoop</artifactId>
    <packaging>jar</packaging>
    <version>1.0-SNAPSHOT</version>
    <name>simple-hadoop</name>
    <url>http://maven.apache.org</url>

    <properties>
        <hadoop.version>2.7.2</hadoop.version>
        <avro.version>1.8.1</avro.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
            <scope>test</scope>
        </dependency>
        <!-- Hadoop -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.avro</groupId>
            <artifactId>avro</artifactId>
            <version>${avro.version}</version>
        </dependency>
        <!-- Required for the  FsInput class  -->
        <dependency>
            <groupId>org.apache.avro</groupId>
            <artifactId>avro-mapred</artifactId>
            <version>${avro.version}</version>
            <classifier>hadoop2</classifier>
        </dependency>

    </dependencies>
    <build>
        <plugins>
            <!-- build an uber jar with hadoop dependencies included -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

</project>

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">

<groupId>com.mytutorial</groupId>

<artifactId>simple-hadoop</artifactId>

<version>1.0-SNAPSHOT</version>

<name>simple-hadoop</name>

<url>http://maven.apache.org</url>

<hadoop.version>2.7.2</hadoop.version>

<avro.version>1.8.1</avro.version>

</properties>

<groupId>junit</groupId>

<artifactId>junit</artifactId>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-hdfs</artifactId>

<version>${hadoop.version}</version>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-client</artifactId>

<version>${hadoop.version}</version>

</dependency>

<groupId>org.apache.avro</groupId>

<version>${avro.version}</version>

</dependency>

<groupId>org.apache.avro</groupId>

<artifactId>avro-mapred</artifactId>

<version>${avro.version}</version>

<classifier>hadoop2</classifier>

</dependency>

</dependencies>

<build>

<groupId>org.apache.maven.plugins</groupId>

<artifactId>maven-shade-plugin</artifactId>

<phase>package</phase>

<goals>

<goal>shade</goal>

</goals>

</execution>

</executions>

</plugin>

</plugins>

</build>

</project>

Step 2: The AVRO files are schema based. … Read more ›...

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: Databricks – Spark SCD Type 2

Prerequisite: Extends 03: Databricks – Spark SCD Type 1. What is SCD Type 2 SCD stands for Slowly Changing Dimension, and it was explained in 10 Data warehouse interview Q&As. … Read more ›...

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: Docker Tutorial – mount a host directory in a Docker container

mount a host directory into a Docker container so that the folders & files in the host will show up in the Docker container after building the dockerfile. There are a couple ways you can do this. Option 1: ADD In this approach, any changes made to this directory on...

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: Drools with rules in an excel spreadsheet – Data validation

This tutorial uses Drools as a validation engine to validate an input csv file. The rules are maintained externally in an excel spreadsheet. Step 1: Create a new Maven project

mvn archetype:generate -DgroupId=com.mytutorial -DartifactId=myDroolsTutorials -B

1	mvn archetype:generate -DgroupId=com.mytutorial -DartifactId=myDroolsTutorials -B

“-B” means interactive mode is false. Import it into eclipse or IDE of your choice as a maven project....

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: Install AWS CLI to intercat with AWS services like S3

Install Python & pip Step 1: Check if python is already installed. Ubuntu ships with both Python 3 and Python 2 pre-installed.


$ python -V
$ python3 -V

$ python -V

$ python3 -V

if already installed, make sure that the versions are up-to-date, as it needs to be either 3.3+ or 2.7+.


$ sudo apt-get update
$ sudo apt-get -y upgrade

$ sudo apt-get update

$ sudo apt-get -y upgrade

If not, … Read more ›...

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: Interfaces – How will you write a program in Golang using interfaces?

Q01. Why use interfaces in Golang?
A01. The key benefit of using interfaces in Go is that they allow for very flexible, extendable and decoupled code. This lead to cleaner & more flexible code. In Go, an interface is a collection of method signatures that define a set of behaviours that a type must implement to be considered “implementing”

…

04: JasperReport generating a PDF file

This is an extension to the JasperReport step by step tutorial (Part-1) to demonstrate how to generate a a PDF file from the Main.java. To generate a PDF file you need the iText.jar as a dependency. Here are the Maven dependency files required.

<!-- Jasper reports -->
<dependency>
 <groupId>net.sf.jasperreports</groupId>
 <artifactId>jasperreports</artifactId>
 <version>5.0.1</version>
</dependency>
<dependency>
 <groupId>org.codehaus.groovy</groupId>
 <artifactId>groovy-all</artifactId>
 <version>2.1.1</version>
</dependency>
<dependency>
 <groupId>com.lowagie</groupId>
 <artifactId>itext</artifactId>
 <version>2.1.7</version>
</dependency>

<groupId>net.sf.jasperreports</groupId>

<artifactId>jasperreports</artifactId>

</dependency>

<groupId>org.codehaus.groovy</groupId>

<artifactId>groovy-all</artifactId>

</dependency>

<groupId>com.lowagie</groupId>

<artifactId>itext</artifactId>

</dependency>

Next, … Read more ›...

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: JAXB tutorial with XSD & binding.xjb

Step 1: Create a “simple-jaxb” project using Maven

mvn archetype:generate -DarchetypeGroupId=org.apache.maven.archetypes -DarchetypeArtifactId=maven-archetype-quickstart -DgroupId=com.jaxb.tutorial -DartifactId=simple-jaxb -Dversion=0.0.1

1	mvn archetype:generate -DarchetypeGroupId=org.apache.maven.archetypes -DarchetypeArtifactId=maven-archetype-quickstart -DgroupId=com.jaxb.tutorial -DartifactId=simple-jaxb -Dversion=0.0.1

Step 2: Import the project into eclipse with File –> Import –> Existing Maven Projects and choose your folder you just created with pom.xml file “simple-jaxb”. Step 3: select “src/main/resources” … Read more ›...

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: Kubernetes concepts like kubectl, contexts, kubeconfig & namespaces

This extends Install Kubernetes (i.e. minikube) on Mac.

Step 1: Start the Docker.app on Mac, and then start the minikube cluster from a terminal.


~]$ minikube start

~]$ minikube start

This creates a local Kubernetes cluster. You can confirm if the Minikube cluster is running with:


~]$ minikube status

~]$ minikube status

Kubectl vs Kubernetes Cluster

A Kubernetes cluster can be created on a cloud like AWS,

…

04: Learn how to connect to HBase from Spark using Java API

These Hadoop tutorials assume that you have installed Cloudera QuickStart, which has the Hadoop eco system like HDFS, Spark, Hive, HBase, YARN, etc. What is HBase? Apache HBase is a NoSQL database used for random and real-time read/write access to your Big Data. It is built on top of the...

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: Running a Simple Spark Job in local & cluster modes

Step 1: Create a simple maven Spark project using “-B” for non-interactive mode.


[cloudera@quickstart ~]$ mvn archetype:generate -DgroupId=com.mytutorial -DartifactId=simple-spark -B

[cloudera@quickstart ~]$ mvn archetype:generate -DgroupId=com.mytutorial -DartifactId=simple-spark -B

Step 2: Import the maven project “simple-spark” into eclipse. Step 3: The pom.xml file should have the relevant dependency jars as shown below.


<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.mytutorial</groupId>
    <artifactId>simple-spark</artifactId>
    <packaging>jar</packaging>
    <version>1.0-SNAPSHOT</version>
    <name>simple-spark</name>
    <url>http://maven.apache.org</url>

    <properties>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <junit.version>4.8.1</junit.version>
        <hadoop.version>2.7.2</hadoop.version>
        <spark-version>1.3.0</spark-version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>${junit.version}</version>
            <scope>test</scope>
        </dependency>
        
        <!-- Hadoop -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>${hadoop.version}</version>
            <exclusions>
                <exclusion>
                    <groupId>javax.servlet</groupId>
                    <artifactId>*</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
            <exclusions>
                <exclusion>
                    <groupId>javax.servlet</groupId>
                    <artifactId>*</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <!-- Apache Spark -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark-version}</version>
            <exclusions>
                <exclusion>
                    <groupId>javax.servlet</groupId>
                    <artifactId>*</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
    </dependencies>
</project>

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">

<groupId>com.mytutorial</groupId>

<artifactId>simple-spark</artifactId>

<version>1.0-SNAPSHOT</version>

<name>simple-spark</name>

<url>http://maven.apache.org</url>

<maven.compiler.source>1.8</maven.compiler.source>

<maven.compiler.target>1.8</maven.compiler.target>

<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>

<junit.version>4.8.1</junit.version>

<hadoop.version>2.7.2</hadoop.version>

<spark-version>1.3.0</spark-version>

</properties>

<groupId>junit</groupId>

<artifactId>junit</artifactId>

<version>${junit.version}</version>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-hdfs</artifactId>

<version>${hadoop.version}</version>

<groupId>javax.servlet</groupId>

</exclusion>

</exclusions>

</dependency>

<groupId>org.apache.hadoop</groupId>

<artifactId>hadoop-client</artifactId>

<version>${hadoop.version}</version>

<groupId>javax.servlet</groupId>

</exclusion>

</exclusions>

</dependency>

<groupId>org.apache.spark</groupId>

<artifactId>spark-core_2.11</artifactId>

<version>${spark-version}</version>

<groupId>javax.servlet</groupId>

</exclusion>

</exclusions>

</dependency>

</dependencies>

</project>

Step 4: Write the simple Spark job “SimpleSparkJob.java” … Read more ›...

This content is for 30-Day-Full-Access, 100-Day-Full-Access, 200-Day-Full-Access, and 365-Day-Full-Access members only. Register 80+ Free Java FAQs 80+ Free Big Data FAQs

Already a member? Log in here

04: Spark on Zeppelin – DataFrame joins in Scala

This tutorial extends the series: Spark on Apache Zeppelin Tutorials.

1. Create “Orders” DataFrame

import org.apache.spark.sql._
import org.apache.spark.sql.types._

val orders = Seq (
    Row ("order-1", "cust-234", 234.50),
    Row ("order-2", "cust-234", 347.00),
    Row ("order-3", "cust-337", 67.00),
    Row ("order-4", "cust-425", 84.50),
    Row ("order-5", "cust-456", 134.50)
)

val orderSchema = List(
  StructField("order_num", StringType, true),
  StructField("customer_num", StringType, true),
  StructField("amount", DoubleType, true)
)

val ordersDf = spark.createDataFrame(sc.parallelize(orders), StructType(orderSchema))

ordersDf.show()

import org.apache.spark.sql._

import org.apache.spark.sql.types._

val orders = Seq (

Row ("order-1", "cust-234", 234.50),

Row ("order-2", "cust-234", 347.00),

Row ("order-3", "cust-337", 67.00),

Row ("order-4", "cust-425", 84.50),

Row ("order-5", "cust-456", 134.50)

)

val orderSchema = List(

StructField("order_num", StringType, true),

StructField("customer_num", StringType, true),

StructField("amount", DoubleType, true)

)

val ordersDf = spark.createDataFrame(sc.parallelize(orders), StructType(orderSchema))

ordersDf.show()


+---------+------------+------+
|order_num|customer_num|amount|
+---------+------------+------+
|  order-1|    cust-234| 234.5|
|  order-2|    cust-234| 347.0|
|  order-3|    cust-337|  67.0|
|  order-4|    cust-425|  84.5|
|  order-5|    cust-456| 134.5|
+---------+------------+------+

+---------+------------+------+

|order_num|customer_num|amount|

+---------+------------+------+

| order-1| cust-234| 234.5|

| order-2| cust-234| 347.0|

| order-3| cust-337| 67.0|

| order-4| cust-425| 84.5|

| order-5| cust-456| 134.5|

+---------+------------+------+

2. Create “Customers” DataFrame

import org.apache.spark.sql._
import org.apache.spark.sql.types._

val customers = Seq (
    Row ("cust-234", "John"),
    Row ("cust-239", "Peter"),
    Row ("cust-337", "Samuel"),
    Row ("cust-425", "Jordan"),
    Row ("cust-456", "George")
)

val customerSchema = List(
  StructField("customer_num", StringType, true),
  StructField("customer_name", StringType, true)
)

val customersDf = spark.createDataFrame(sc.parallelize(customers), StructType(customerSchema))

customersDf.show()

import org.apache.spark.sql._

import org.apache.spark.sql.types._

val customers = Seq (

Row ("cust-234", "John"),

Row ("cust-239", "Peter"),

Row ("cust-337", "Samuel"),

Row ("cust-425", "Jordan"),

Row ("cust-456", "George")

)

val customerSchema = List(

StructField("customer_num", StringType, true),

StructField("customer_name", StringType, true)

)

val customersDf = spark.createDataFrame(sc.parallelize(customers), StructType(customerSchema))

customersDf.show()


+------------+-------------+
|customer_num|customer_name|
+------------+-------------+
|    cust-234|         John|
|    cust-239|        Peter|
|    cust-337|       Samuel|
|    cust-425|       Jordan|
|    cust-456|       George|
+------------+-------------+

+------------+-------------+

|customer_num|customer_name|

+------------+-------------+

| cust-234| John|

| cust-239| Peter|

| cust-337| Samuel|

| cust-425| Jordan|

| cust-456| George|

+------------+-------------+

You can perform a number of joins between DataFrames. Default is the inner join. Joins can be of: inner,

…

Blog Archives

04: Convert XML file To an Avro File with Apache Spark – writing & reading

04: Create new or append to an existing AVRO file tutorial

04: Databricks – Spark SCD Type 2

04: Docker Tutorial – mount a host directory in a Docker container

04: Drools with rules in an excel spreadsheet – Data validation

04: Install AWS CLI to intercat with AWS services like S3

04: Interfaces – How will you write a program in Golang using interfaces?

04: JasperReport generating a PDF file

04: JAXB tutorial with XSD & binding.xjb

04: Kubernetes concepts like kubectl, contexts, kubeconfig & namespaces

04: Learn how to connect to HBase from Spark using Java API

04: Running a Simple Spark Job in local & cluster modes

04: Spark on Zeppelin – DataFrame joins in Scala

300+ Java Interview Questions - FAQs

10+ Companion Techs Interview Q&As

300+ Big Data Interview Questions - FAQs

16+ Java Tech Key Areas Interview Q&As

Your Career Tips to go places

Tutorials on Java & Big Data

300+ Java Interview Questions Answered

Disclaimer