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 is a unified analytics platform that combines data engineering and data science capabilities. It allows users to set up distributed infrastructure and execute data workflows seamlessly. Key features include collaborative notebooks, machine learning support, real-time data processing, and integration with popular data sources. However, Databricks can be costly, especially for large-scale usage, and there might be limitations in terms of customization and control over infrastructure.

  1. Apache Spark: Apache Spark is an open-source distributed computing system that provides processing for large-scale data sets. Key features include in-memory processing, compatibility with multiple programming languages, and a rich set of libraries. Pros include its high performance and extensibility, while cons might involve a steeper learning curve compared to Databricks.
  2. Google Cloud Dataproc: Google Cloud Dataproc is a managed Spark and Hadoop service that allows users to run big data analytics and machine learning workloads. Features include scalability, easy integration with other Google Cloud services, and cost-effectiveness. Pros include seamless integration with Google Cloud ecosystem, while limitations may involve less control compared to Databricks.
  3. AWS EMR: Amazon EMR is a managed big data platform on AWS that allows users to process large amounts of data using Apache Spark and other big data frameworks. Key features include flexibility, scalability, and seamless integration with other AWS services. Pros include deep integration with AWS, while cons may involve complex setup and maintenance compared to Databricks.
  4. Alteryx: Alteryx is a self-service analytics platform that offers data blending, advanced analytics, and machine learning capabilities. Features include drag-and-drop interface, automation of data workflows, and predictive analytics. Pros include ease of use and comprehensive analytics functionalities, while cons may involve less emphasis on big data processing compared to Databricks.
  5. Cloudera: Cloudera is a big data platform that provides tools for data engineering, data warehousing, and machine learning. Key features include scalability, security, and support for a variety of data processing frameworks. Pros include comprehensive big data solutions, while cons could be complexity and setup overhead compared to Databricks.
  6. IBM Watson Studio: IBM Watson Studio is an integrated environment for data scientists, developers, and domain experts to collaboratively and easily work with data and to build and train models at scale. Features include visual modeling tools, automatic model generation, and seamless data integration. Pros include IBM's cognitive capabilities and enterprise-grade security, while cons may include a higher learning curve for beginners compared to Databricks.
  7. Talend: Talend is a cloud data integration and data integrity platform that enables users to connect, cleanse, and combine data from different sources. Key features include data quality tools, real-time data integration, and self-service data preparation. Pros include ease of use and flexibility in data integration, while cons may involve less focus on advanced analytics compared to Databricks.
  8. Qubole: Qubole is a cloud-native, self-service big data platform that enables users to quickly process and analyze big data workloads. Features include auto-scaling, integrations with popular data processing engines, and self-service data exploration. Pros include ease of use and cost-effectiveness, while cons may involve limited customization options compared to Databricks.
  9. Snowflake: Snowflake is a cloud-based data platform that provides data warehousing, data lake, and data sharing capabilities. Key features include scalability, performance, and ease of use. Pros include simplicity in managing data and querying, while cons may involve less emphasis on advanced analytics and machine learning compared to Databricks.
  10. H2O.ai: H2O.ai is an open-source machine learning platform that offers automatic machine learning, model management, and interpretable machine learning. Features include scalability, ease of use, and support for popular machine learning algorithms. Pros include a strong focus on machine learning capabilities, while cons may involve less comprehensive data engineering tools compared to Databricks.

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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    I'm wondering if any Cloud Firestore users might be open to sharing some input and challenges encountered when trying to create a low-cost, low-latency data pipeline to their Analytics warehouse (e.g. Google BigQuery, Snowflake, etc...)

    I'm working with a platform by the name of Estuary.dev, an ETL/ELT and we are conducting some research on the pain points here to see if there are drawbacks of the Firestore->BQ extension and/or if users are seeking easy ways for getting nosql->fine-grained tabular data

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    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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    Azure Databricks logo

    Azure Databricks

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    Fast, easy, and collaborative Apache Spark–based analytics service
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        Domino logo

        Domino

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            I have recently started using Confluent/Kafka cloud. We want to do some stream processing. As I was going through Kafka I came across Kafka Streams and KSQL. Both seem to be A good fit for stream processing. But I could not understand which one should be used and one has any advantage over another. We will be using Confluent/Kafka Managed Cloud Instance. In near future, our Producers and Consumers are running on premise and we will be interacting with Confluent Cloud.

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            Apache Spark logo

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            Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 12.2M views

            How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

            Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

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            (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

            Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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            Eric Colson
            Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 6.1M 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.

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            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.

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            #DataScience #DataStack #Data

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            Azure HDInsight logo

            Azure HDInsight

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