Author: Divyansh Jain

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

How to write Clean Code- Part2

Reading Time: 3 minutes In our previous blog, we discussed some of the tips to write clean code. However, those are not just enough for better programming. Instead, there are some points to take care which helps in writing more better code. And, here in part 2, we will majorly focus on those points which were not been covered in Part1 such as Exception Handling, Data Structures & Unit Continue Reading

How to write Clean Code- Part1

Reading Time: 4 minutes Well, everyone in the Programming world wants to have good coding skills and definitely wants to write good code. Even while writing this blog, I have a mindset of becoming a better programmer and further following the best practices. So, today in this blog will be sharing some of the steps of better programming which will be very helpful to write easy and understandable code. Continue Reading

Apache Spark: Handle Corrupt/Bad Records

Reading Time: 3 minutes Most of the time writing ETL jobs becomes very expensive when it comes to handling corrupt records. And in such cases, ETL pipelines need a good solution to handle corrupted records. Because, larger the ETL pipeline is, the more complex it becomes to handle such bad records in between. Corrupt data includes: Missing information Incomplete information Schema mismatch Differing formats or data types Apache Spark: Continue Reading

Amazon EMR

Reading Time: 3 minutes Businesses worldwide are discovering the power of new big data processing and analytics frameworks like Apache Hadoop and Apache Spark, but they are also discovering some of the challenges of operating these technologies in on-premises data lake environments. They may also have concerns about the future of their current distribution vendor. Common problems of on-premises big data environments include a lack of agility, excessive costs, 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

Apache Spark: Read Data from S3 Bucket

Reading Time: < 1 minute Amazon S3 Accessing S3 Bucket through Spark Edit spark-default.conf file You need to add below 3 lines consists of your S3 access key, secret key & file system

Apache Spark: Repartitioning v/s Coalesce

Reading Time: 3 minutes Does partitioning help you increase/decrease the Job Performance? Spark splits data into partitions and computation is done in parallel for each partition. It is very important to understand how data is partitioned and when you need to manually modify the partitioning to run spark applications efficiently. Now, diving into our main topic i.e Repartitioning v/s Coalesce What is Coalesce? The coalesce method reduces the number Continue Reading

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

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

Defining your workflow: Why Not Airflow?

Reading Time: 4 minutes What is Apache Airflow? Airflow is a platform to programmatically author, schedule & monitor workflows or data pipelines. These functions achieved with Directed Acyclic Graphs (DAG) of the tasks. It is an open-source and still in the incubator stage. It was initialized in 2014 under the umbrella of Airbnb since then it got an excellent reputation with approximately 800 contributors on GitHub and 13000 stars. Continue Reading

Big Data Evolution: Migrating on-premise database to Hadoop

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