Alternatives to Azure HDInsight logo

Alternatives to Azure HDInsight

Amazon EMR, Azure Databricks, Hadoop, Azure Machine Learning, and Azure Data Factory are the most popular alternatives and competitors to Azure HDInsight.
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What is Azure HDInsight and what are its top alternatives?

It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
Azure HDInsight is a tool in the Big Data as a Service category of a tech stack.

Top Alternatives to Azure HDInsight

  • Amazon EMR

    Amazon EMR

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

  • Azure Databricks

    Azure Databricks

    Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service. ...

  • Hadoop

    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

  • Azure Machine Learning

    Azure Machine Learning

    Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning. ...

  • Azure Data Factory

    Azure Data Factory

    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. ...

  • Databricks

    Databricks

    Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications. ...

  • Apache Spark

    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

    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. ...

Azure HDInsight alternatives & related posts

Amazon EMR logo

Amazon EMR

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Distribute your data and processing across a Amazon EC2 instances using Hadoop
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PROS OF AMAZON EMR
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    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
    Elastic
  • 4
    Backed by Amazon
  • 3
    Flexible
  • 3
    Economic - pay as you go, easy to use CLI and SDKs
  • 2
    Don't need a dedicated Ops group
  • 1
    Great support
  • 1
    Massive data handling
CONS OF AMAZON EMR
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    related Amazon EMR posts

    Azure Databricks logo

    Azure Databricks

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    Fast, easy, and collaborative Apache Spark–based analytics service
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    PROS OF AZURE DATABRICKS
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      CONS OF AZURE DATABRICKS
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        related Azure Databricks posts

        Hadoop logo

        Hadoop

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        Open-source software for reliable, scalable, distributed computing
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        PROS OF HADOOP
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          Great ecosystem
        • 11
          One stack to rule them all
        • 4
          Great load balancer
        • 1
          Amazon aws
        • 1
          Java syntax
        CONS OF HADOOP
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          related Hadoop posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1.1M views

          Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

          Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

          https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

          (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

          See more
          Shared insights
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          KafkaKafkaHadoopHadoop
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          The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

          For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

          Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

          See more
          Azure Machine Learning logo

          Azure Machine Learning

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          A fully-managed cloud service for predictive analytics
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          PROS OF AZURE MACHINE LEARNING
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            CONS OF AZURE MACHINE LEARNING
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              related Azure Machine Learning posts

              Azure Data Factory logo

              Azure Data Factory

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              Hybrid data integration service that simplifies ETL at scale
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              PROS OF AZURE DATA FACTORY
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                CONS OF AZURE DATA FACTORY
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                  related Azure Data Factory posts

                  Vamshi Krishna
                  Data Engineer at Tata Consultancy Services · | 4 upvotes · 111.4K views

                  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?

                  See more
                  Databricks logo

                  Databricks

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                  A unified analytics platform, powered by Apache Spark
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                  PROS OF DATABRICKS
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                    Best Performances on large datasets
                  • 1
                    True lakehouse architecture
                  • 1
                    Scalability
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                    Databricks doesn't get access to your data
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                    Usage Based Billing
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                    Security
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                    Data stays in your cloud account
                  • 1
                    Multicloud
                  CONS OF DATABRICKS
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                    related Databricks posts

                    Apache Spark logo

                    Apache Spark

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                    Fast and general engine for large-scale data processing
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                    PROS OF APACHE SPARK
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                      Open-source
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                      Fast and Flexible
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                      One platform for every big data problem
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                      Easy to install and to use
                    • 6
                      Great for distributed SQL like applications
                    • 3
                      Works well for most Datascience usecases
                    • 2
                      Machine learning libratimery, Streaming in real
                    • 2
                      In memory Computation
                    • 0
                      Interactive Query
                    CONS OF APACHE SPARK
                    • 3
                      Speed

                    related Apache Spark posts

                    Eric Colson
                    Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.1M views

                    The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

                    Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

                    At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

                    For more info:

                    #DataScience #DataStack #Data

                    See more
                    Conor Myhrvold
                    Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1.1M views

                    Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

                    Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

                    https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

                    (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

                    See more
                    Amazon Redshift logo

                    Amazon Redshift

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                    Fast, fully managed, petabyte-scale data warehouse service
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                    PROS OF AMAZON REDSHIFT
                    • 37
                      Data Warehousing
                    • 27
                      Scalable
                    • 17
                      SQL
                    • 14
                      Backed by Amazon
                    • 5
                      Encryption
                    • 1
                      Cheap and reliable
                    • 1
                      Isolation
                    • 1
                      Best Cloud DW Performance
                    • 1
                      Fast columnar storage
                    CONS OF AMAZON REDSHIFT
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                      related Amazon Redshift posts

                      Julien DeFrance
                      Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

                      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

                      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

                      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

                      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

                      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

                      Future improvements / technology decisions included:

                      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

                      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

                      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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                      Ankit Sobti

                      Looker , Stitch , Amazon Redshift , dbt

                      We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

                      For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

                      See more