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  5. Apache Spark vs Azure Databricks

Apache Spark vs Azure Databricks

OverviewDecisionsComparisonAlternatives

Overview

Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K
Azure Databricks
Azure Databricks
Stacks252
Followers396
Votes0

Apache Spark vs Azure Databricks: What are the differences?

Introduction

Apache Spark and Azure Databricks are both popular tools for big data processing and analytics, but they have key differences that distinguish them in terms of performance, ease of use, and integration with other services.

  1. Programming Language Support: Apache Spark mainly supports Scala, Java, and Python, while Azure Databricks extends its support to R as well, providing more flexibility in coding languages for data processing and analysis tasks.

  2. Integration with Azure Services: Azure Databricks is tightly integrated with various Azure services such as Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Cosmos DB, making it easier to seamlessly work with other Azure components in a unified data platform.

  3. Collaboration and Sharing: Azure Databricks offers built-in collaboration features like shared notebooks, job scheduling, and interactive dashboards, enabling efficient teamwork and sharing of insights among data scientists and analysts.

  4. Unified Workspace: Azure Databricks provides a unified workspace for data engineering, collaborative data science, and business analytics, enabling a seamless transition between different activities without the need for separate tools or environments.

  5. Cost Management: Azure Databricks has built-in cost management tools that help monitor and optimize usage to control costs effectively, whereas Apache Spark does not offer such built-in cost management features, requiring manual monitoring and optimization efforts.

  6. Security and Compliance: Azure Databricks provides enhanced security features like Azure Active Directory integration, role-based access control, and encryption at rest, ensuring compliance with industry and organizational security standards more easily compared to Apache Spark.

In Summary, Azure Databricks offers extended language support, integration with Azure services, enhanced collaboration tools, unified workspace, cost management features, and improved security and compliance compared to Apache Spark.

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Advice on Apache Spark, Azure Databricks

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

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
Apache Spark
Azure Databricks
Azure 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.

Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.

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
Optimized Apache Spark environment; Autoscale and auto terminate; Collaborative workspace; Optimized for deep learning; Integration with Azure services; Support for multiple languages and libraries
Statistics
GitHub Stars
42.2K
GitHub Stars
-
GitHub Forks
28.9K
GitHub Forks
-
Stacks
3.1K
Stacks
252
Followers
3.5K
Followers
396
Votes
140
Votes
0
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
No community feedback yet
Integrations
No integrations available
Scala
Scala
Azure DevOps
Azure DevOps
Databricks
Databricks
Python
Python
GitHub
GitHub
.NET for Apache Spark
.NET for Apache Spark

What are some alternatives to Apache Spark, Azure Databricks?

Google Analytics

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

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

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

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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

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

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

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.

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