Hadoop and Databricks
October 30, 2020

The 21st century has so far been an era of extremely large technological advances, but above all, we have been able to realize that these advances all had communication at their core.

Using wireless technologies that involved disruptive hardware and software has allowed the human race to connect with vast numbers of people, having a profound impact on business, politics, and culture. These advances have transformed our lives entirely to a point at which it is hard to even imagine going back.

So in this age of emerging technologies, the amount of data is increasing very rapidly and due to this massive increase of data, the level of computations are also increasing,  so time has changed and innovation has also gotten so advanced; Today we are going to have a close look at Hadoop and Databricks as two distributed application and techniques.

First, let us understand that Hadoop is an open-source, Java-based software platform that manages data processing and storage for big data applications. Hadoop works by distributing large data sets and analytics jobs across nodes in a computing cluster, breaking them down into smaller workloads that can be run in parallel. Hadoop can process both structured and unstructured data, and scale up reliably from a single server to thousands of machines.

Hadoop was born out of a need to process escalating volumes of big data. Inspired by Google’s MapReduce, a programming model that divides an application into small fractions to run on different nodes,

On the other hand; Databricks is an Apache Spark-based analytics platform optimized for the Microsoft cloud services platform. It is integrated with Azure to provide a one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and also business analysts

Hadoop and Databricks have now made it possible for companies to analyze and query big data sets in a scalable manner using free, open-source software and inexpensive, off-the-shelf hardware.

Below is the comparative analysis of Hadoop and Databricks in a tabular form






Scalability: Hadoop is scalable in its operation in a distributed environment, unlike the traditional systems that have a limitation on data storage, and this allowed data architects to build early data lakes on Hadoop.  The system is extremely scalable.  One of its greatest features and a big selling point is that It enables a company to scale or expand easily. Even a company that does’ t work directly or day to day with Databricks still requires daily usage from the solution due to the fact that when they schedule everything they need, it will always trigger works that need to be done. You do not even need to log into the database console every day, all you need is to configure it one time and that’s it. Then it will deliver everything needed in the time required.
Resilience:  The fact is that any data stored on any node of the Hadoop cluster will also be replicated on other nodes of the cluster to prepare for the possibility of hardware or software failures.  This intentionally redundant design ensures fault tolerance. The whole idea is that if one goes down, there is always backup data available in the cluster.  The Hadoop Distributed File System (HDFS) is fundamentally resilient. It is important to understand that with cloud services, and with the help of Databricks, we can use any computer of our choice when running our code. This offers considerable scope for testing different algorithms, carrying out data transformation, or large calculations. Installing your own cluster, which is such a time-consuming job, becomes a mere click.
Flexibility and usability:  Unlike traditional relational database management systems, when working with Hadoop you can store data in any format, including semi-structured or unstructured formats. Hadoop enables businesses to easily access new data sources and tap into different types of data. This has been very useful in organizations for shared notebooks, integrated data pipeline automation, and data sources integrations. Integration with AWS is seamless and this is to say that non-tech users can easily learn how to use Databricks.  Data processing clusters can be configured and deployed with just a few clicks. The platform includes varied built-in data visualization features to graph data.
Performance:  Hadoop uses frequent reads and writes to disk to perform computations, which is time-consuming and inefficient compared to frameworks that aim to store and process data in memory as much as possible, like Apache Spark.  Databricks is known for providing quicker calculations and answers because code execution is distributed between several computers (known as a cluster), thus offering better utilization of available computer power in a cloud.


Availability:  Hadoop is well-suited to enterprise-class data lakes, or large data repositories that require high-availability and super-fast access. Hadoop lends itself to administrators that are well versed in Linux as well. Hadoop is not well suited to situations that don’t care about high-availability or don’t have any Linux or Hadoop admin available. Databricks became immensely available and popular immediately cloud services took off. It is also possible to use Jupyter in various cloud services such as Google GCP, Amazon AWS, and Microsoft Azure, but recently Databricks has quite simply become more widely known and available.
Capability: Hadoop is very well suited for big data modeling problems in various industries like finance, insurance, healthcare, automobiles, CRM, etc. In every industry where you need data analysis in real-time, Hadoop is a perfect fit in terms of storage, analysis, retrieval, and processing. It won’t be a very good tool to perform ETL (Extract Transform Load) techniques though. Databricks has helped my teams write PySpark and Spark SQL jobs and test them out before formally integrating them in Spark jobs. Through Databricks we can create parquet and JSON output files. Data modelers and scientists who are not very good with coding can get a good insight into the data using the notebooks that can be developed by the engineers
Hadoop has a reach ecosystem of companion tools that enable easy integration for ingesting large amounts of data efficiently from various sources. For example, Apache Flume can act as a data bus which can use HDFS as a sink and integrates effectively with disparate data sources.

Currently, many organizations are looking to shed the costs and frustration of their Hadoop implementation by migrating to a cloud-based system. Because they believe that Hadoop was not designed for cloud implementations, and they also think that lifting and shifting to Hadoop in the cloud brings a lot of the same limitations and frustration. But this is not true because Hadoop is a perfect fit in terms of storage, analysis, retrieval, and processing.

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