Insights and Perspectives to keep you updated
Reading Time: 6 minutes Delta Lake is an open-source storage layer that brings reliability to data lakes. It has numerous reliability features including ACID transactions, scalable metadata handling, and unified streaming and batch data processing.
Reading Time: 4 minutes Welcome Back. In our previous blogs, we tried to get some insights about Spark RDDs and also tried to explore some new things in Spark 2.4. You can go through those blogs here: RDDs – The backbone of Apache Spark Spark 2.4: Adding a little more Spark to your code In this blog, we will be discussing something called a Delta Lake. But first, let’s Continue Reading
Reading Time: 4 minutes We are now generating massive volumes of data at an accelerated rate. To meet business needs, address changing market dynamics as well as improve decision-making, sophisticated analysis of this data from disparate sources is required. The challenge is how to capture, store and model these massive pools of data effectively in relational databases. Big data is not a fad. We are just at the beginning Continue Reading
Reading Time: 3 minutes In two previous blogs, we explored about Vertica and how it can be connected to Apache Spark. The first blog in this mini series was about reading data from Vertica using Spark and saving that data into Kafka. The next blog explained the reverse flow i.e. reading data from Kafka and writing data to Vertica but in a batch mode. i.e reading data from Kafka Continue Reading
Reading Time: 4 minutes In previous blog of this series, we took a glance over the basic definition of Spark and Vertica. We also did a code overview for reading data from Vertica using Spark as DataFrame and saving the data into Kafka. In this blog we will be doing the reverse flow i.e. working on reading the data from Kafka as a DataFrame and writing that DataFrame into Continue Reading
Reading Time: 4 minutes We live in a world of Big data where the size of data is so big even for small results. This is the result of an increase in data collection on a rapid scale in the modern world. This massiveness of data brings the requirements of such tools which can work upon such a big chunk of data. I am pretty sure that you guys Continue Reading
Reading Time: 5 minutes With the massive amount of increase in big data technologies today, it is becoming very important to use the right tool for every process. The process can be anything like Data ingestion, Data processing, Data retrieval, Data Storage, etc. Today we are going to focus on one of those popular big data technologies i.e., Apache Spark. Apache Spark is an open-source distributed general-purpose cluster-computing framework. Spark Continue Reading
Reading Time: 6 minutes Fan of Apache Spark? I am too. The reason is simple. Interesting APIs to work with, fast and distributed processing, unlike map-reduce no I/O overhead, fault tolerance and many more. With this much, you can do a lot in this world of Big data and Fast data. From “processing huge chunks of data” to “working on streaming data”, Spark works flawlessly in all. In this Continue Reading
Reading Time: 4 minutes optimize indexing
bulk indexing elasticsearch
Reading Time: 5 minutes 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
Reading Time: 3 minutes 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
Reading Time: 5 minutes 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
Reading Time: 3 minutes 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
Sarfaraz Hussain
Aakash Jain
Abhishek Baranwal
Alok Jha
Anjali Sharma
Ankur Thakur
Anmol Sarna
Anuj Saxena
Ayush Hooda
Ayushi Hasija
Ayush Mishra
Ayush Prashar
Ayush_Singhal
Azmat Hasan
Bhavya Aggarwal
Bhawna Sharma
Piyush Rana
Pawan Bisht
Shubham Verma
Charmy Garg
Harshit Daga
Rudar
Deepak Mehra
Deepankar
Divyansh Jain
Ayush Tiwari
etash2901
Gautam Singal
girdharshubham
Girish Bharti
Harmeet Singh(Taara)
Himani Arora
Himanshu Gupta
Sonu Mehrotra
Nancy Jain
Jashan Goyal
Jasmine Kaur
Jatin Demla
Juoko Virtanen
Rachel Jones
JustinB
Jyoti Sachdeva
Kamlesh prajapati
Rishi Khandelwal
Manish Mishra
Vikas Hazrati
Komal Rajpal
Kritika Khandelwal
kundankumarr
Sahil Sawhney
Megha Jatana
Mahesh Chand
Mayank Bairagi
Miral Gandhi
Mukesh Kumar
munandersingh
Kaushik Nath
Narayan Kumar
Neelam Lakra
Neelu Aggarwal
Neeraj Chugh
Neha Bhardwaj
Niharika Datta
Nitin Arora
Pankaj Chaudhary
Prabhat Kashyap
Pranjut Gogoi
Prashant Sharma
Priyanka Thakur
Rahul Khanna
Ramandeep
Ram Indukuri
Randhir kumar
Joseph Ross
Ruchika Dubey
Rajeev Thakur
Shivangi Gupta
Shubham Goyal
Shubham Dangare
Sidharth Khattri
Sachin Slathia
GauravShukla
Shubhrank Rastogi
Sudeep James Tirkey
Swantika Gupta
Nitin Aggarwal
kunal sethi
Vandana Yadav
Vidisha Gupta
Vinay Kumar
Vinisha Sharma
Yatharth Sharma
Knoldus is the world's largest pure-play Scala and Spark company. We modernize enterprise through cutting-edge digital engineering by leveraging Scala, Functional Java and Spark ecosystem. Our mission is to provide reactive and streaming fast data solutions that are message-driven, elastic, resilient, and responsive.
+(1) 647-467-4396
hello@knoldus.com
