Big Data Analytics

Flink: Union operator on Multiple Streams

Reading Time: 3 minutes Apache Flink offers rich sources of API and operators which makes Flink application developers productive in terms of dealing with the multiple data streams. Flink provides many multi streams operations like Union, Join, and so on. In this blog, we will explore the Union operator in Flink that can combine two or more data streams together. We know in real-time we can have multiple data streams from different sources Continue Reading

Flink: Implementing the Session window.

Reading Time: 3 minutes In the previous blogs, we learned about Tumbling, Sliding, and Count windows in Flink. There is one another useful way to window the data which Flink offers i.e, Session window. So in this blog, we will explore the Session window in detail with an example. In the real world, all the work that we do online- Visiting a website, Clicking around the website, do online Continue Reading

Flink: Implementing the Count Window

Reading Time: 3 minutes In the blog, we learned about Tumbling and Sliding windows which is based on time. In this blog, we are going to learn to define Flink’s windows on other properties i.e Count window. As the name suggests, count window is evaluated when the number of records received, hits the threshold. Count window set the window size based on how many entities exist within that window. For example, if we fixed the count Continue Reading

Basic Anatomy of a Flink Program

Reading Time: 3 minutes Hi Folks! Hope you all are safe in the COVID-19 pandemic and learning new tools and tech while staying at home. I also have just started learning a very prominent Big Data framework for stream processing which is  Flink. Flink is a distributed framework and based on the streaming first principle, means it is a real streaming processing engine and implements batch processing as a special case. In Continue Reading

Windows operator: Heart of processing infinite streams in Flink

Reading Time: 3 minutes Apache Flink is an open-source, distributed, Big Data framework for stream and batch data processing. Flink is based on the streaming first principle which means it is a real streaming processing engine and implements batching as a special case. Flink is considered to have a heart and it is the “Windows” operator. It makes Flink capable of processing infinite streams quickly and efficiently. Windows split Continue Reading

Spark: Streaming Datasets

Reading Time: 3 minutes Spark providing us a high-level API – Dataset, which makes it easy to get type safety and securely perform manipulation in a distributed and a local environment without code changes. Also, spark structured streaming, a high-level API for stream processing allows us to stream a particular Dataset which is nothing but a type-safe structured streams. In this blog, we will see how we can create Continue Reading

Delta Lake: Schema Enforcement & Evolution

Reading Time: 4 minutes Nowadays data is constantly evolving and changing. As well as the business problems and requirements are evolving, the shape or the structure of the data is also changing. When that happens, we want to be in control of how the data or schema changes. But how we can achieve this? Delta Lake has good ways to control how schema changes. With Delta Lake, users have Continue Reading

fetching data from different sources using Spark 2.1

Spark: createDataFrame() vs toDF()

Reading Time: 2 minutes There are two different ways to create a Dataframe in Spark. First, using toDF() and second is using createDataFrame(). In this blog we will see how we can create Dataframe using these two methods and what’s the exact difference between them. toDF() toDF() method provides a very concise way to create a Dataframe. This method can be applied to a sequence of objects. To access Continue Reading

Cluster vs Client: Execution modes for a Spark application

Reading Time: 3 minutes Whenever we submit a Spark application to the cluster, the Driver or the Spark App Master should get started. And the Driver will be starting N number of workers. Spark driver will be managing spark context object to share the data and coordinates with the workers and cluster manager across the cluster. Cluster Manager can be Spark Standalone or Hadoop YARN or Mesos. Workers will Continue Reading

Apache Spark: Delta Lake as a Solution – Part II

Reading Time: 3 minutes Well, we have already covered the missing features in Apache Spark & also the causes of the issue in executing Delta Lake in Part1. However, today we will be talking about What Delta Lake is & how it provides the solution to all those problems discussed herein Delta Lake as a Solution: Part1.As we all know that Spark is just a processing engine, it doesn’t Continue Reading

Apache Spark: Delta Lake as a Solution – Part I

Reading Time: 3 minutes Today, everyone is talking about Delta Lake. Why? Ever tried to find the answer to this question? Yes or No doesn’t matter, don’t worry here in Part1 we will be discussing the same & also will be targetting the following questions: What are the features missing from Apache Spark? What kind of issues it causes in executing Data Lake? Answering the above questions will definitely Continue Reading

top 7 data analytics trends

Top 7 Data Analytics and Management Trends for 2020

Reading Time: 5 minutes We live in an era of data as it lies at the heart of digital transformation. And datasets are no longer as simple as before. They have increased in volumes, velocity, complexity and above all, are coming from multiple sources. Top tech giants like Google, Netflix, Amazon, and others are crunching massive amounts of data on a daily basis to give you a personalized experience. Continue Reading

Apache Spark: Tricks to Increase Job Performance

Reading Time: 2 minutes Apache Spark is quickly adopting the Real-world and most of the companies like Uber are using it in their production. Spark is gaining its popularity in the market as it also provides you with the feature of developing Streaming Applications and doing Machine Learning, which helps companies get better results in their production along with proper analysis using Spark. Although companies are using Spark in Continue Reading