Apache Spark vs Azure Synapse

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Azure Synapse

Stacks104

Followers230

Votes10

Apache Spark vs Azure Synapse: What are the differences?

Introduction

Apache Spark and Azure Synapse are both powerful data processing platforms used in big data analytics. While they have several similarities, there are key differences that set them apart.

Execution Framework: Apache Spark is built on top of the Spark execution engine, which provides in-memory distributed data processing capabilities. On the other hand, Azure Synapse leverages the distributed processing capabilities of Azure Data Lake Analytics for executing big data workloads.
Integration with Azure Services: Azure Synapse offers tight integration with various Azure services, such as Azure Data Factory, Azure Machine Learning, and Azure Databricks. This integration allows seamless data movement, transformation, and analytics across different Azure services. Apache Spark, on the other hand, is not specifically designed for the Azure ecosystem and may require additional setup and configuration to integrate with Azure services.
Data Warehouse Capabilities: Azure Synapse is primarily designed as a unified analytics platform, combining enterprise data warehousing and big data processing capabilities. It provides a fully-managed SQL-based data warehouse, allowing users to query and analyze structured and semi-structured data. Apache Spark, on the other hand, is more focused on big data processing and provides a flexible, distributed computing framework.
Scalability and Performance: Both Apache Spark and Azure Synapse are designed for scalability and can handle large-scale data processing. However, Azure Synapse leverages the underlying scalability and performance capabilities of the Azure platform, making it well-suited for processing massive amounts of data. Apache Spark provides distributed computing capabilities, but it may require additional tuning and configuration for optimal performance.
Pricing and Cost Model: Apache Spark is an open-source project and can be used for free. However, when using it in a cloud environment, there may be additional costs for compute resources and storage. Azure Synapse, on the other hand, is a managed service offered by Microsoft and follows a metered pricing model based on usage. The pricing for Azure Synapse includes compute resources, storage, and data transfer costs.
Development and Programming Paradigm: Apache Spark supports multiple programming languages, including Scala, Python, Java, and R. It offers a rich set of APIs and libraries for data processing, machine learning, and streaming analytics. Azure Synapse, on the other hand, primarily focuses on SQL-based development and provides integration with T-SQL and PolyBase for querying and manipulating data.

In summary, Apache Spark and Azure Synapse are both powerful data processing platforms, but they differ in terms of execution framework, integration with Azure services, data warehouse capabilities, scalability and performance, pricing and cost model, and development and programming paradigm.

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Advice on Apache Spark, Azure Synapse

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.

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Detailed Comparison

Apache Spark	Azure Synapse
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 an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
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	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 104
Followers 3.5K	Followers 230
Votes 140	Votes 10
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 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Concurrency 1 Dictionary Size Limitation - CCI

What are some alternatives to Apache Spark, Azure Synapse?

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

Related Comparisons

Apache Spark vs Azure Synapse: What are the differences?

Introduction

Apache Spark and Azure Synapse are both powerful data processing platforms used in big data analytics. While they have several similarities, there are key differences that set them apart.

Execution Framework: Apache Spark is built on top of the Spark execution engine, which provides in-memory distributed data processing capabilities. On the other hand, Azure Synapse leverages the distributed processing capabilities of Azure Data Lake Analytics for executing big data workloads.
Integration with Azure Services: Azure Synapse offers tight integration with various Azure services, such as Azure Data Factory, Azure Machine Learning, and Azure Databricks. This integration allows seamless data movement, transformation, and analytics across different Azure services. Apache Spark, on the other hand, is not specifically designed for the Azure ecosystem and may require additional setup and configuration to integrate with Azure services.
Data Warehouse Capabilities: Azure Synapse is primarily designed as a unified analytics platform, combining enterprise data warehousing and big data processing capabilities. It provides a fully-managed SQL-based data warehouse, allowing users to query and analyze structured and semi-structured data. Apache Spark, on the other hand, is more focused on big data processing and provides a flexible, distributed computing framework.
Scalability and Performance: Both Apache Spark and Azure Synapse are designed for scalability and can handle large-scale data processing. However, Azure Synapse leverages the underlying scalability and performance capabilities of the Azure platform, making it well-suited for processing massive amounts of data. Apache Spark provides distributed computing capabilities, but it may require additional tuning and configuration for optimal performance.
Pricing and Cost Model: Apache Spark is an open-source project and can be used for free. However, when using it in a cloud environment, there may be additional costs for compute resources and storage. Azure Synapse, on the other hand, is a managed service offered by Microsoft and follows a metered pricing model based on usage. The pricing for Azure Synapse includes compute resources, storage, and data transfer costs.
Development and Programming Paradigm: Apache Spark supports multiple programming languages, including Scala, Python, Java, and R. It offers a rich set of APIs and libraries for data processing, machine learning, and streaming analytics. Azure Synapse, on the other hand, primarily focuses on SQL-based development and provides integration with T-SQL and PolyBase for querying and manipulating data.

Apache Spark vs Azure Synapse

Overview

Apache Spark vs Azure Synapse: What are the differences?