Apache Spark vs Azure Data Factory

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

Apache Spark vs Azure Data Factory: What are the differences?

Apache Spark and Azure Data Factory are two popular data processing platforms that are used for big data analytics and processing. While both platforms offer similar functionalities, there are some key differences that set them apart from each other.

Data Processing Approach: One key difference between Apache Spark and Azure Data Factory is their approach to data processing. Apache Spark is a distributed computing system that utilizes in-memory processing to perform fast and efficient data processing tasks. On the other hand, Azure Data Factory is a cloud-based data integration service that focuses on orchestrating and managing data pipelines for batch and real-time data movement and transformation.
Language Support: Another difference between Apache Spark and Azure Data Factory is the programming languages supported by each platform. Apache Spark provides support for multiple programming languages including Scala, Java, Python, and R, making it a versatile platform for data processing and machine learning tasks. Azure Data Factory, on the other hand, primarily relies on Azure Data Lake Analytics, a separate service that supports U-SQL, a language specifically designed for big data processing.
Data Processing Capabilities: Apache Spark offers a wide range of built-in libraries and APIs that provide various data processing capabilities such as data streaming, machine learning, graph processing, and SQL querying. This allows users to perform complex data analytics tasks within a single platform. Azure Data Factory, on the other hand, offers limited data processing capabilities and relies on other Azure services, such as Azure Data Lake Analytics and HDInsight, for advanced data processing tasks.
Deployment Options: Apache Spark offers flexible deployment options, allowing users to run Spark on various platforms including standalone mode, on a cluster, or on cloud providers such as Amazon Web Services (AWS) and Microsoft Azure. Azure Data Factory, being a cloud-based service, is primarily deployed on the Microsoft Azure platform, making it suitable for organizations already using Azure services or looking for a fully managed cloud solution.
Data Integration and Ecosystem: Apache Spark has a robust ecosystem, with integration capabilities for various data sources, such as Hadoop Distributed File System (HDFS), Apache Kafka, Apache Cassandra, and more. This allows users to seamlessly integrate with existing data sources and systems. Azure Data Factory, on the other hand, offers native integration with various Azure services, such as Azure Blob Storage, Azure SQL Database, Azure Cosmos DB, and more, making it a suitable choice for organizations heavily relying on Microsoft Azure services.

In summary, Apache Spark and Azure Data Factory differ in their approach to data processing, language support, data processing capabilities, deployment options, and data integration and ecosystem. These differences make each platform suitable for different use cases and requirements, depending on the organization's infrastructure, data sources, and preferences.

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, Azure Data Factory

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

269k views269k

Comments

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	Azure Data Factory
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.	It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.
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	Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Statistics
GitHub Stars 42.2K	GitHub Stars 516
GitHub Forks 28.9K	GitHub Forks 610
Stacks 3.1K	Stacks 253
Followers 3.5K	Followers 484
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	Octotree Java .NET

What are some alternatives to Apache Spark, Azure Data Factory?

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.

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.

Apache Camel

An open source Java framework that focuses on making integration easier and more accessible to developers.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Related Comparisons

Apache Spark vs Azure Data Factory: What are the differences?

Data Processing Approach: One key difference between Apache Spark and Azure Data Factory is their approach to data processing. Apache Spark is a distributed computing system that utilizes in-memory processing to perform fast and efficient data processing tasks. On the other hand, Azure Data Factory is a cloud-based data integration service that focuses on orchestrating and managing data pipelines for batch and real-time data movement and transformation.
Language Support: Another difference between Apache Spark and Azure Data Factory is the programming languages supported by each platform. Apache Spark provides support for multiple programming languages including Scala, Java, Python, and R, making it a versatile platform for data processing and machine learning tasks. Azure Data Factory, on the other hand, primarily relies on Azure Data Lake Analytics, a separate service that supports U-SQL, a language specifically designed for big data processing.
Data Processing Capabilities: Apache Spark offers a wide range of built-in libraries and APIs that provide various data processing capabilities such as data streaming, machine learning, graph processing, and SQL querying. This allows users to perform complex data analytics tasks within a single platform. Azure Data Factory, on the other hand, offers limited data processing capabilities and relies on other Azure services, such as Azure Data Lake Analytics and HDInsight, for advanced data processing tasks.
Deployment Options: Apache Spark offers flexible deployment options, allowing users to run Spark on various platforms including standalone mode, on a cluster, or on cloud providers such as Amazon Web Services (AWS) and Microsoft Azure. Azure Data Factory, being a cloud-based service, is primarily deployed on the Microsoft Azure platform, making it suitable for organizations already using Azure services or looking for a fully managed cloud solution.
Data Integration and Ecosystem: Apache Spark has a robust ecosystem, with integration capabilities for various data sources, such as Hadoop Distributed File System (HDFS), Apache Kafka, Apache Cassandra, and more. This allows users to seamlessly integrate with existing data sources and systems. Azure Data Factory, on the other hand, offers native integration with various Azure services, such as Azure Blob Storage, Azure SQL Database, Azure Cosmos DB, and more, making it a suitable choice for organizations heavily relying on Microsoft Azure services.

Apache Spark vs Azure Data Factory

Overview