Spark

Understanding Spark’s Logical and Physical Plan in layman’s term

Reading Time: 5 minutes This blog pertains to Apache SPARK 2.x, where we will find out how Spark SQL works internally in layman’s terms and try to understand what is Logical and Physical Plan. Also we will be looking into Catalyst Optimizer. So let’s get started. First let’s see what Apache Spark is. The official definition of Apache Spark says that “Apache Spark™ is a unified analytics engine for large-scale Continue Reading

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Apache Spark

Deep Dive into Apache Spark Transformations and Action

Reading Time: 4 minutes In our previous blog of Apache Spark, we discussed a little about what Transformations & Actions are? Now we will get deeper into the topic and will understand what actually they are & how they play a vital role to work with Apache Spark? What is Spark RDD? Spark introduces the concept of an RDD (Resilient Distributed Dataset), an immutable fault-tolerant, distributed collection of objects Continue Reading

Diving deeper into Delta Lake

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.

Delta Lake To the Rescue

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

Spark – Actions and Transformations

Reading Time: 4 minutes Hey guys, welcome to series of spark blogs, this blog being the first blog in this series we would try to keep things as crisp as possible, so let’s get started. So I recently get to start learning spark about believe me and now it has made me inquisitive about it, for a brief introduction of spark, I would say that it is a pretty Continue Reading

Tale of Apache Spark

Reading Time: 6 minutes Data is being produced extensively in today’s world and it is going to be generated more rapidly in future. 90% of total data that is produced in the world is produced in last two years only and it is estimated that in 2020 world’s total data would reach 45 ZB and data generated each day would be enough that if we try to store it Continue Reading

Using Vertica with Spark-Kafka: Write using Structured Streaming

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

Using Vertica with Spark-Kafka: Writing

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

Do you really need Spark? Think Again!

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

Spark: Introduction to Datasets

Reading Time: 3 minutes As I have already discussed in my previous blog Spark: RDD vs DataFrames about the shortcomings of RDDs and how DataFrames overcome them. Now we’ll try to have a look at the shortcomings of DataFrames and how Dataset APIs can overcome them. DataFrames:- A DataFrame is a distributed collection of data, which is organized into named columns. Conceptually, it is equivalent to the relational tables with Continue Reading

Spark Streaming vs. Structured Streaming

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

Spark: RDD vs DataFrames

Reading Time: 3 minutes Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimizations.One use of Spark SQL is to execute SQL queries. When running SQL from within another Continue Reading

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