Optimizing Indexing speed in Elasticsearch with Spark

Manish MishraJanuary 27, 2019January 27, 2019Apache Spark, Scala, SparkApache Spark, Benchmark, elasticsearch, indexing

optimize indexing
bulk indexing elasticsearch

Apache Spark 2.4: Adding a little more Spark to your code

Anmol SarnaDecember 16, 2018Apache Spark, Java, Scala, Spark, StreamingApache Spark, avro, higher order function, scala, spark 2.4

Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark recently released its fifth release in the 2.x version line i.e Spark 2.4. We were lucky enough to experiment with it so soon in one of our projects. Today we will try to highlight the major changes in this version that we explored as well as experienced in our project. In our Continue Reading

Spark Structured Streaming with Elasticsearch

Anuj SaxenaSeptember 25, 2018Apache Spark, Big Data and Fast Data, Elasticsearch, Scala, Streaming, Streaming SolutionsApache Spark, elasticsearch, Elasticsearch with structured streaming, Streaming, Structured Streaming

There’s been a lot of time we have been working on streaming data. Using Apache Spark for that can be much convenient. Spark provides two APIs for streaming data one is Spark Streaming which is a separate library provided by Spark. Another one is Structured Streaming which is built upon the Spark-SQL library. We will discuss the trade-offs and differences between these two libraries in Continue Reading

CuriosityX: RDDs – The backbone of Apache Spark

Anmol SarnaJuly 30, 2018July 31, 2018Apache Spark, HDFS, Java, Scala, SparkApache Spark, Hadoop, java, partitioning in apache spark, RDD, scala, Spark

In our last blog, we tried to understand about using the spark streaming to transform and transport data between Kafka topics. After reading that many of the readers asked us to give a brief description of RDDs in Spark which we used. So, this blog is totally dedicated to the RDDs in Spark. So let’s start with the very basic question that comes to our mind Continue Reading

They said Spark Streaming simply means Discretized Stream

Nitin AggarwalMarch 23, 2018March 23, 2018Apache Spark, Scala, SparkApache Spark, scala, Spark, spark 2.0, technology

I am working in a company (Knoldus Software LLP) where Apache Spark is literally running into people’s blood means there are certain people who are really good at it. If you ever visit our blogging page and search for stuff related to spark, you will find enough content which is capable of solving your most of spark related queries, starting form introductions to solutions for Continue Reading

Difference between Apache Hadoop and Apache Spark Mapreduce

Geetika GuptaDecember 1, 2017August 9, 2018Apache Spark, Big Data and Fast Data, Scalaapache hadoop, Apache Spark, Big Data, Hadoop, scala, Spark

The term Big Data has created a lot of hype already in the business world. Hadoop and Spark are both Big Data frameworks – they provide some of the most popular tools used to carry out common Big Data-related tasks. In this blog, we will cover what is the difference between Apache Hadoop and Apache Spark MapReduce. Introduction Spark – It is an open source Continue Reading

Assimilation of Spark Streaming With Kafka

Charmy GargNovember 27, 2017August 8, 2018Apache Kafka, Apache Spark, ScalaApache Kafka, Apache Spark, scala, Spark Streaming

As we know Spark is used at a wide range of organizations to process large datasets. It seems like spark becoming main stream. In this blog we will talk about Assimilation of Spark Streaming With Kafka. So, lets get started. How Kafka can be integrated with Spark? Kafka provides a messaging and integration platform for Spark streaming. Kafka act as the central hub for real-time streams of Continue Reading

What’s new in Apache Spark 2.2

Geetika GuptaOctober 4, 2017August 9, 2018Apache Spark, Big Data and Fast Data, Scala, Spark, Streamingapache, Apache Spark, Big Data, opensource, scala, Spark, Spark SQL, Spark Streaming, Spark Structured Streaming, spark2.2, technology

Apache recently released a newer version of Spark i.e Apache Spark 2.2. The new version comes with new improvements as well as the addition of new functionalities. The major addition to this release is Structured Streaming. It has been marked as production ready and its experimental tag has been removed. Some of the high-level changes and improvements : Production ready Structured Streaming Expanding SQL functionalities New Continue Reading

Spark Structured Streaming: A Simple Definition

Himanshu GuptaJune 18, 2017July 20, 2018Scala, Spark, StreamingApache Spark, Big Data, continuous application, spark 2.0, Structured Streaming

“Structured Streaming”, nowadays we are hearing this term in Apache Spark ecosystem quite a lot, as it is being preached as next big thing in scalable big data world. Although, we all know that Structured Streaming means a stream having structured data in it, but very few of us knows what exactly it is and where we can use it. So, in this blog post Continue Reading

Getting Started with the Apache Spark

Charmy GargMay 9, 2017August 8, 2018Apache Spark, Scala, SparkApache Spark, installation of spark on linux, introduction to spark, Spark, spark architecture, spark ecosystem, Spark Streaming

Apache Spark is an open source big data processing framework built around speed, ease of use, and sophisticated analytics. It was originally developed in 2009 in UC Berkeley’s AMPLab, and open sourced in 2010 as an Apache project. Spark has several advantages compared to other big data and Map Reduce technologies like Hadoop and Storm. Apache Spark is an improvement on the original Hadoop MapReduce Continue Reading

The Dominant APIs of Spark: Datasets, DataFrames and RDDs

Pallavi SinghApril 16, 2017April 17, 2017SparkApache Spark, dataframes, datasets, performance optimization, RDD, space optimization, spark apis

While working with Spark often we come across the three APIs: DataFrames, Datasets and RDDs. In this blog I will discuss the three in terms of use case, performance and optimization. It is essential to keep in mind that there is seamless transformation available between the three DataFrames, Datasets and RDDs. Implicitly the RDD forms the apex of both DataFrame and Datasets. The inception of Continue Reading

Partition-Aware Data Loading in Spark SQL

Himanshu GuptaFebruary 9, 2017July 20, 2018Scala, SparkApache Spark, Best Practices, Big Data, Spark SQL

Data loading, in Spark SQL, means loading data in memory/cache of Spark worker nodes. For which we use to write following code: val connectionProperties = new Properties() connectionProperties.put(“user”, “username”) connectionProperties.put(“password”, “password”) val jdbcDF = spark.read .jdbc(“jdbc:postgresql:dbserver”, “schema.table”, connectionProperties) In here we are using jdbc function of DataFrameReader API of Spark SQL to load the data from table into Spark Executor’s memory, no matter how many rows are Continue Reading

Migration From Spark 1.x to Spark 2.x

Anurag SrivastavaDecember 30, 2016December 31, 2016ScalaApache Spark

Hello Folks, As we know that we have latest release of Spark 2.0, with to much enhancement and new features. If you are using Spark 1.x and now you want to move your application with Spark 2.0 that time you have to take care for some changes which happened in the API. In this blog we are going to get an overview of common changes: Continue Reading

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