Apache Spark vs Databricks

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Databricks

Stacks525

Followers768

Votes8

Apache Spark vs Databricks: What are the differences?

Introduction

Apache Spark and Databricks are both widely used in big data processing and analytics. While Apache Spark is an open-source distributed computing system, Databricks is a unified analytics platform built on top of Apache Spark. Despite their similarities, there are key differences between the two.

Integration and Collaboration: Databricks provides a collaborative environment where multiple data scientists, analysts, and engineers can work together seamlessly. It offers features like notebooks, dashboards, and shared workspaces for enhanced collaboration. In contrast, Apache Spark lacks built-in collaboration tools and requires additional setup to achieve similar functionalities.
Managed Services: Databricks is a managed service in the cloud, offered by the company Databricks, where customers can easily deploy and scale their Spark applications without worrying about infrastructure management. On the other hand, Apache Spark needs to be deployed and managed by organizations themselves, either on-premises or in the cloud, which requires more effort and expertise.
Automation and Integration: Databricks provides automation and integration features that simplify the deployment and management of Spark applications. It offers automated cluster management and integration with various data sources and tools such as AWS, Azure, and Tableau. While Apache Spark can also be integrated with other tools, it requires more manual configuration and setup.
Security and Compliance: Databricks provides advanced security features like role-based access control, encryption, and compliance certifications that ensure data protection and meet industry standards. Apache Spark, being open-source, lacks some of these advanced security features out-of-the-box, although it can be enhanced using third-party solutions and custom implementations.
Cost Structure: Databricks follows a subscription-based pricing model, where customers pay for the usage of the platform based on resources consumed. This includes the managed infrastructure, support, and additional features provided by Databricks. In contrast, Apache Spark is open-source and free to use, but organizations need to bear the costs of infrastructure, maintenance, and support themselves.
Enterprise Support and Services: Databricks offers comprehensive enterprise support, including 24/7 technical assistance, training, and consulting services. They also have partnerships with major cloud providers like AWS and Azure, providing customers with a seamless experience. While Apache Spark has a large community and many resources available, enterprise-level support and services are generally not provided directly by the Apache Spark project.

In summary, Databricks provides a managed, collaborative, and feature-rich platform built on top of Apache Spark, whereas Apache Spark itself requires more manual configuration and lacks some of the advanced features and support provided by Databricks.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Apache Spark, Databricks

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k

Comments

Detailed Comparison

Apache Spark	Databricks
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.	Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	Built on Apache Spark and optimized for performance; Reliable and Performant Data Lakes; Interactive Data Science and Collaboration; Data Pipelines and Workflow Automation; End-to-End Data Security and Compliance; Compatible with Common Tools in the Ecosystem; Unparalled Support by the Leading Committers of Apache Spark
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 525
Followers 3.5K	Followers 768
Votes 140	Votes 8
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 1 Security 1 Usage Based Billing 1 Databricks doesn't get access to your data 1 Scalability 1 Data stays in your cloud account
Integrations
No integrations available	MLflow Delta Lake Kafka TensorFlow Hadoop PyTorch Keras

What are some alternatives to Apache Spark, Databricks?

Google Analytics

Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.

Mixpanel

Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience.

Piwik

Matomo (formerly Piwik) is a full-featured PHP MySQL software program that you download and install on your own webserver. At the end of the five-minute installation process, you will be given a JavaScript code.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Clicky

Clicky Web Analytics gives bloggers and smaller web sites a more personal understanding of their visitors. Clicky has various features that helps stand it apart from the competition specifically Spy and RSS feeds that allow web site owners to get live information about their visitors.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Related Comparisons

Apache Spark vs Databricks: What are the differences?

Introduction

Integration and Collaboration: Databricks provides a collaborative environment where multiple data scientists, analysts, and engineers can work together seamlessly. It offers features like notebooks, dashboards, and shared workspaces for enhanced collaboration. In contrast, Apache Spark lacks built-in collaboration tools and requires additional setup to achieve similar functionalities.
Managed Services: Databricks is a managed service in the cloud, offered by the company Databricks, where customers can easily deploy and scale their Spark applications without worrying about infrastructure management. On the other hand, Apache Spark needs to be deployed and managed by organizations themselves, either on-premises or in the cloud, which requires more effort and expertise.
Automation and Integration: Databricks provides automation and integration features that simplify the deployment and management of Spark applications. It offers automated cluster management and integration with various data sources and tools such as AWS, Azure, and Tableau. While Apache Spark can also be integrated with other tools, it requires more manual configuration and setup.
Security and Compliance: Databricks provides advanced security features like role-based access control, encryption, and compliance certifications that ensure data protection and meet industry standards. Apache Spark, being open-source, lacks some of these advanced security features out-of-the-box, although it can be enhanced using third-party solutions and custom implementations.
Cost Structure: Databricks follows a subscription-based pricing model, where customers pay for the usage of the platform based on resources consumed. This includes the managed infrastructure, support, and additional features provided by Databricks. In contrast, Apache Spark is open-source and free to use, but organizations need to bear the costs of infrastructure, maintenance, and support themselves.
Enterprise Support and Services: Databricks offers comprehensive enterprise support, including 24/7 technical assistance, training, and consulting services. They also have partnerships with major cloud providers like AWS and Azure, providing customers with a seamless experience. While Apache Spark has a large community and many resources available, enterprise-level support and services are generally not provided directly by the Apache Spark project.

Apache Spark vs Databricks

Overview

Apache Spark vs Databricks: What are the differences?