Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Azure Synapse

Stacks104

Followers230

Votes10

Apache Flink vs Azure Synapse: What are the differences?

Introduction: In the comparison between Apache Flink and Azure Synapse, there are key differences that differentiate these two technologies in terms of their capabilities and use cases.

1. Performance and Scalability: Apache Flink is known for its low latency and high throughput processing capabilities, making it suitable for real-time stream processing and event-driven applications. On the other hand, Azure Synapse is designed for large-scale data warehousing and analytics, offering capabilities for processing massive amounts of batch data efficiently.

2. Supported Use Cases: Apache Flink is commonly used for real-time data streaming, interactive analytics, and machine learning applications due to its high-performance processing engine. Azure Synapse, on the other hand, is primarily used for data warehousing, big data analytics, and business intelligence applications, offering comprehensive tools for data integration and visualization.

3. Programming Languages: Apache Flink supports multiple programming languages including Java, Scala, and Python, providing developers with flexibility in choosing their preferred language for application development. In contrast, Azure Synapse primarily supports SQL-based querying language for data exploration and analysis, limiting the programming languages available for development.

4. Deployment and Management: Apache Flink can be deployed on various cloud platforms and on-premises environments, enabling organizations to have flexibility in choosing their deployment options. Azure Synapse, as a cloud-based service, offers easy deployment and management through the Azure portal, providing scalability and reliability for data processing tasks.

5. Integration with Ecosystem: Apache Flink has strong integration with popular big data ecosystem tools such as Apache Kafka, Apache Hadoop, and Apache Beam, making it easier for organizations to build end-to-end data processing pipelines. Azure Synapse, being part of the Azure ecosystem, integrates seamlessly with other Azure services like Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Machine Learning.

6. Cost Consideration: Apache Flink is an open-source framework, which means it is free to use and deploy in production environments, making it a cost-effective option for organizations looking to build real-time processing applications. On the other hand, Azure Synapse is a cloud-based service that requires payment based on usage and resources consumed, making it more suitable for organizations with specific budget considerations.

In Summary, the key differences between Apache Flink and Azure Synapse lie in their performance, supported use cases, programming languages, deployment options, ecosystem integrations, and cost considerations.

🔥 Trending in Databases on StackShare

pgvector Databases

Open-source vector similarity search for Postgres

Try it View Docs Alternatives

Try

189

7

Advice on Apache Flink, 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.

576k views576k

Comments

Detailed Comparison

Apache Flink	Azure Synapse
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.	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.
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
GitHub Stars 25.4K	GitHub Stars -
GitHub Forks 13.7K	GitHub Forks -
Stacks 534	Stacks 104
Followers 879	Followers 230
Votes 38	Votes 10
Pros & Cons
Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Dictionary Size Limitation - CCI 1 Concurrency
Integrations
YARN Hadoop Hadoop HBase Kafka	No integrations available

What are some alternatives to Apache Flink, 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.

Apache Spark

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.

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.

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.