Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Azure HDInsight

Stacks29

Followers138

Votes0

Apache Flink vs Azure HDInsight: What are the differences?

Key Differences between Apache Flink and Azure HDInsight

Apache Flink and Azure HDInsight are both powerful big data processing platforms, but they have several key differences that set them apart.

1. Execution Model: Apache Flink follows a distributed dataflow model where tasks are executed as a directed acyclic graph (DAG). On the other hand, Azure HDInsight uses the Hadoop MapReduce model for executing jobs. This difference in execution models affects how the platforms handle tasks and how they optimize resource utilization.

2. Real-time Stream Processing: Apache Flink specializes in real-time stream processing and has built-in support for event time processing, windowing, and stateful computations. Azure HDInsight, on the other hand, is focused on batch processing and lacks the same level of support for real-time stream processing.

3. Data Sources and Sinks: Apache Flink provides a wide range of connectors to various data sources and sinks, including Kafka, HDFS, JDBC, and more. Azure HDInsight, being a cloud-based service, integrates well with other Azure services, such as Azure Blob Storage and Azure Data Lake Storage, but may have limited connectivity options compared to Flink.

4. Language Support: Apache Flink supports multiple programming languages, including Java, Scala, and Python, allowing developers to choose the language they are most comfortable with. Azure HDInsight, on the other hand, primarily supports Java and Scala, which may restrict the choice of programming language for developers.

5. Integration with Ecosystem: Apache Flink integrates well with other big data ecosystem tools like Apache Kafka, Apache Hadoop, and Apache Hive. It can be seamlessly integrated as a processing engine within these ecosystems. In comparison, Azure HDInsight is part of the Azure ecosystem and provides tight integration with other Azure services like Azure Storage, Azure SQL Database, and Azure Machine Learning.

6. Deployment and Management: Apache Flink is an open-source project that can be deployed on any infrastructure, be it on-premises or in the cloud. It offers more deployment flexibility and allows for customization. Azure HDInsight, being a managed service, abstracts away much of the deployment and management complexity, making it easier to set up and maintain.

In summary, Apache Flink excels in real-time stream processing, offers a broader range of data sources and sinks, supports multiple programming languages, and provides more flexibility in deployment options. Azure HDInsight, on the other hand, integrates well with the Azure ecosystem, offers a managed service experience, and focuses primarily on batch processing.

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Advice on Apache Flink, Azure HDInsight

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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Comments

Detailed Comparison

Apache Flink	Azure HDInsight
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 a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
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	Fully managed; Full-spectrum; Open-source analytics service in the cloud for enterprises
Statistics
GitHub Stars 25.4K	GitHub Stars -
GitHub Forks 13.7K	GitHub Forks -
Stacks 534	Stacks 29
Followers 879	Followers 138
Votes 38	Votes 0
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	No community feedback yet
Integrations
YARN Hadoop Hadoop HBase Kafka	IntelliJ IDEA Apache Spark Kafka Visual Studio Code Hadoop Apache Storm HBase Apache Hive Azure Data Factory Azure Active Directory

What are some alternatives to Apache Flink, Azure HDInsight?

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.

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.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.