Apache Spark vs Azure HDInsight

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Azure HDInsight

Stacks29

Followers138

Votes0

Apache Spark vs Azure HDInsight: What are the differences?

Distribution and Scalability: Apache Spark is a distributed processing system that allows users to process large-scale datasets in parallel. It is designed to be highly scalable and can handle workloads on clusters of thousands of nodes. Azure HDInsight, on the other hand, is a fully-managed cloud service that provides Apache Hadoop, Spark, and other big data processing frameworks. It leverages the scalability of the Azure cloud platform to handle large-scale data processing tasks.
Ease of Use and Flexibility: Spark provides a user-friendly API that allows developers to write applications in multiple languages such as Scala, Java, Python, and R. It offers a rich set of libraries and tools for data analytics, machine learning, and graph processing. Azure HDInsight, being a managed service, simplifies the deployment and management of Spark clusters. It integrates well with other Azure services and provides an intuitive user interface for managing and monitoring Spark jobs.
Integration with Azure Services: HDInsight provides tight integration with other Azure services such as Azure Storage, Azure Data Lake Storage, Azure Active Directory, and Azure SQL Database. This enables users to easily ingest, store, and analyze data from various sources within the Azure ecosystem. Spark can seamlessly read and write data to/from these Azure services, making it easier to build end-to-end data pipelines.
Advanced Analytics and Machine Learning: Spark has built-in support for advanced analytics and machine learning through its MLlib library. It provides a wide range of algorithms for classification, regression, clustering, and recommendation. Azure HDInsight extends Spark's machine learning capabilities by integrating with other Azure services such as Azure Machine Learning and Azure Databricks. This allows users to leverage the power of these services for building and deploying advanced ML models at scale.
Security and Compliance: HDInsight provides robust security features such as role-based access control (RBAC), Azure Active Directory integration, network isolation, and encryption at rest. It also helps organizations meet compliance requirements by supporting data governance frameworks like GDPR, HIPAA, and ISO 27001. Spark, on the other hand, provides fine-grained security controls through features like authentication, authorization, and encryption. It can integrate with external systems for user authentication and access control.
Pricing and Cost Optimization: Apache Spark is an open-source framework and can be used for free. However, the cost of deploying, configuring, and managing Spark clusters can add up for organizations. Azure HDInsight provides a pay-as-you-go pricing model, allowing users to optimize costs by scaling clusters up or down based on workload demands. It also offers cost management features like automatic scaling, cluster resizing, and instance type selection to ensure efficient resource utilization.

In Summary, Apache Spark and Azure HDInsight differ in terms of distribution and scalability, ease of use and flexibility, integration with Azure services, advanced analytics and machine learning capabilities, security and compliance features, and pricing and cost optimization.

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Advice on Apache Spark, 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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Detailed Comparison

Apache Spark	Azure HDInsight
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 cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
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	Fully managed; Full-spectrum; Open-source analytics service in the cloud for enterprises
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 29
Followers 3.5K	Followers 138
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	IntelliJ IDEA Kafka Visual Studio Code Hadoop Apache Storm HBase Apache Hive Azure Data Factory Azure Active Directory

What are some alternatives to Apache Spark, 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.

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.

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.

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.

Related Comparisons

Apache Spark vs Azure HDInsight: What are the differences?

Distribution and Scalability: Apache Spark is a distributed processing system that allows users to process large-scale datasets in parallel. It is designed to be highly scalable and can handle workloads on clusters of thousands of nodes. Azure HDInsight, on the other hand, is a fully-managed cloud service that provides Apache Hadoop, Spark, and other big data processing frameworks. It leverages the scalability of the Azure cloud platform to handle large-scale data processing tasks.
Ease of Use and Flexibility: Spark provides a user-friendly API that allows developers to write applications in multiple languages such as Scala, Java, Python, and R. It offers a rich set of libraries and tools for data analytics, machine learning, and graph processing. Azure HDInsight, being a managed service, simplifies the deployment and management of Spark clusters. It integrates well with other Azure services and provides an intuitive user interface for managing and monitoring Spark jobs.
Integration with Azure Services: HDInsight provides tight integration with other Azure services such as Azure Storage, Azure Data Lake Storage, Azure Active Directory, and Azure SQL Database. This enables users to easily ingest, store, and analyze data from various sources within the Azure ecosystem. Spark can seamlessly read and write data to/from these Azure services, making it easier to build end-to-end data pipelines.
Advanced Analytics and Machine Learning: Spark has built-in support for advanced analytics and machine learning through its MLlib library. It provides a wide range of algorithms for classification, regression, clustering, and recommendation. Azure HDInsight extends Spark's machine learning capabilities by integrating with other Azure services such as Azure Machine Learning and Azure Databricks. This allows users to leverage the power of these services for building and deploying advanced ML models at scale.
Security and Compliance: HDInsight provides robust security features such as role-based access control (RBAC), Azure Active Directory integration, network isolation, and encryption at rest. It also helps organizations meet compliance requirements by supporting data governance frameworks like GDPR, HIPAA, and ISO 27001. Spark, on the other hand, provides fine-grained security controls through features like authentication, authorization, and encryption. It can integrate with external systems for user authentication and access control.
Pricing and Cost Optimization: Apache Spark is an open-source framework and can be used for free. However, the cost of deploying, configuring, and managing Spark clusters can add up for organizations. Azure HDInsight provides a pay-as-you-go pricing model, allowing users to optimize costs by scaling clusters up or down based on workload demands. It also offers cost management features like automatic scaling, cluster resizing, and instance type selection to ensure efficient resource utilization.

Apache Spark vs Azure HDInsight

Overview