Alternatives to Databricks logo

Alternatives to Databricks

Snowflake, Azure Databricks, Domino, Confluent, and Apache Spark are the most popular alternatives and competitors to Databricks.
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What is Databricks and what are its top alternatives?

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.
Databricks is a tool in the General Analytics category of a tech stack.

Top Alternatives to Databricks

  • Snowflake

    Snowflake

    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn. ...

  • 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. ...

  • Domino

    Domino

    Use our cloud-hosted infrastructure to securely run your code on powerful hardware with a single command — without any changes to your code. If you have your own infrastructure, our Enterprise offering provides powerful, easy-to-use cluster management functionality behind your firewall. ...

  • Confluent

    Confluent

    It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream ...

  • 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. ...

  • Azure HDInsight

    Azure HDInsight

    It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data. ...

  • Splunk

    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Qubole

    Qubole

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

Databricks alternatives & related posts

Snowflake logo

Snowflake

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The data warehouse built for the cloud
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PROS OF SNOWFLAKE
  • 3
    Good Performance
  • 2
    User Friendly
  • 2
    Serverless
  • 2
    Great Documentation
  • 2
    Multicloud
  • 2
    Public and Private Data Sharing
  • 1
    Usage based billing
  • 1
    Innovative
  • 1
    Economical
CONS OF SNOWFLAKE
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    Shared insights
    on
    Google BigQueryGoogle BigQuerySnowflakeSnowflake

    I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

    What's nice too is that it has SQL-based ML tools, and it has great GIS support!

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    Shared insights
    on
    SnowflakeSnowflakeHadoopHadoopMarkLogicMarkLogic

    For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

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    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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        Domino logo

        Domino

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        A PaaS for data science - easily run R, Python or Matlab code in the cloud with automatic...
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        PROS OF DOMINO
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          CONS OF DOMINO
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            Confluent logo

            Confluent

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            A stream data platform to help companies harness their high volume real-time data streams
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            PROS OF CONFLUENT
            • 2
              No hypercloud lock-in
            • 2
              Dashboard for kafka insight
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              Zero devops
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              Free for casual use
            • 1
              Easily scalable
            CONS OF CONFLUENT
            • 1
              Proprietary

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            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
            • 48
              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

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            Eric Colson
            Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2M 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

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            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 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 )

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            Azure HDInsight logo

            Azure HDInsight

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            A cloud-based service from Microsoft for big data analytics
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            PROS OF AZURE HDINSIGHT
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                Splunk logo

                Splunk

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                Search, monitor, analyze and visualize machine data
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                PROS OF SPLUNK
                • 2
                  API for searching logs, running reports
                • 1
                  Query engine supports joining, aggregation, stats, etc
                • 1
                  Query any log as key-value pairs
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                  Splunk language supports string, date manip, math, etc
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                  Granular scheduling and time window support
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                  Alert system based on custom query results
                • 1
                  Custom log parsing as well as automatic parsing
                • 1
                  Dashboarding on any log contents
                • 1
                  Ability to style search results into reports
                • 1
                  Rich GUI for searching live logs
                CONS OF SPLUNK
                • 1
                  Splunk query language rich so lots to learn

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                Shared insights
                on
                KibanaKibanaSplunkSplunkGrafanaGrafana

                I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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                Qubole logo

                Qubole

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                Prepare, integrate and explore Big Data in the cloud (Hive, MapReduce, Pig, Presto, Spark and Sqoop)
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                PROS OF QUBOLE
                • 13
                  Simple UI and autoscaling clusters
                • 10
                  Feature to use AWS Spot pricing
                • 7
                  Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters
                • 7
                  Real-time data insights through Spark Notebook
                • 6
                  Hyper elastic and scalable
                • 6
                  Easy to manage costs
                • 6
                  Easy to configure, deploy, and run Hadoop clusters
                • 4
                  Backed by Amazon
                • 4
                  Gracefully Scale up & down with zero human intervention
                • 2
                  All-in-one platform
                • 2
                  Backed by Azure
                CONS OF QUBOLE
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