Alternatives to Azure Databricks logo

Alternatives to Azure Databricks

Databricks, Azure Machine Learning, Azure HDInsight, Apache Spark, and Snowflake are the most popular alternatives and competitors to Azure Databricks.
168
266
+ 1
0

What is Azure Databricks and what are its top alternatives?

Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
Azure Databricks is a tool in the General Analytics category of a tech stack.

Top Alternatives to Azure Databricks

  • 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. ...

  • 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 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. ...

  • 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. ...

  • 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 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. ...

  • Azure Functions

    Azure Functions

    Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems. ...

  • Google Analytics

    Google Analytics

    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications. ...

Azure Databricks alternatives & related posts

Databricks logo

Databricks

292
477
8
A unified analytics platform, powered by Apache Spark
292
477
+ 1
8
PROS OF DATABRICKS
  • 1
    Best Performances on large datasets
  • 1
    True lakehouse architecture
  • 1
    Scalability
  • 1
    Databricks doesn't get access to your data
  • 1
    Usage Based Billing
  • 1
    Security
  • 1
    Data stays in your cloud account
  • 1
    Multicloud
CONS OF DATABRICKS
    Be the first to leave a con

    related Databricks posts

    Azure Machine Learning logo

    Azure Machine Learning

    207
    302
    0
    A fully-managed cloud service for predictive analytics
    207
    302
    + 1
    0
    PROS OF AZURE MACHINE LEARNING
      Be the first to leave a pro
      CONS OF AZURE MACHINE LEARNING
        Be the first to leave a con

        related Azure Machine Learning posts

        Azure HDInsight logo

        Azure HDInsight

        25
        109
        0
        A cloud-based service from Microsoft for big data analytics
        25
        109
        + 1
        0
        PROS OF AZURE HDINSIGHT
          Be the first to leave a pro
          CONS OF AZURE HDINSIGHT
            Be the first to leave a con

            related Azure HDInsight posts

            Apache Spark logo

            Apache Spark

            2.4K
            2.8K
            132
            Fast and general engine for large-scale data processing
            2.4K
            2.8K
            + 1
            132
            PROS OF APACHE SPARK
            • 58
              Open-source
            • 48
              Fast and Flexible
            • 7
              One platform for every big data problem
            • 6
              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 · 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

            See more
            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 )

            See more
            Snowflake logo

            Snowflake

            648
            773
            16
            The data warehouse built for the cloud
            648
            773
            + 1
            16
            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
              Be the first to leave a con

              related Snowflake posts

              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!

              See more
              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?

              See more
              Azure Data Factory logo

              Azure Data Factory

              159
              315
              0
              Hybrid data integration service that simplifies ETL at scale
              159
              315
              + 1
              0
              PROS OF AZURE DATA FACTORY
                Be the first to leave a pro
                CONS OF AZURE DATA FACTORY
                  Be the first to leave a con

                  related Azure Data Factory posts

                  Vamshi Krishna
                  Data Engineer at Tata Consultancy Services · | 4 upvotes · 102K 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
                  Azure Functions logo

                  Azure Functions

                  483
                  524
                  40
                  Listen and react to events across your stack
                  483
                  524
                  + 1
                  40
                  PROS OF AZURE FUNCTIONS
                  • 12
                    Pay only when invoked
                  • 8
                    Great developer experience for C#
                  • 6
                    Multiple languages supported
                  • 5
                    Great debugging support
                  • 2
                    Poor developer experience for C#
                  • 2
                    Easy scalability
                  • 2
                    Can be used as lightweight https service
                  • 1
                    WebHooks
                  • 1
                    Event driven
                  • 1
                    Azure component events for Storage, services etc
                  CONS OF AZURE FUNCTIONS
                  • 1
                    No persistent (writable) file system available
                  • 1
                    Poor support for Linux environments
                  • 1
                    Sporadic server & language runtime issues
                  • 1
                    Not suited for long-running applications

                  related Azure Functions posts

                  Kestas Barzdaitis
                  Entrepreneur & Engineer · | 16 upvotes · 451.4K views

                  CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

                  CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

                  AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

                  It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

                  The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

                  In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

                  Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

                  See more
                  Michal Nowak

                  In a couple of recent projects we had an opportunity to try out the new Serverless approach to building web applications. It wasn't necessarily a question if we should use any particular vendor but rather "if" we can consider serverless a viable option for building apps. Obviously our goal was also to get a feel for this technology and gain some hands-on experience.

                  We did consider AWS Lambda, Firebase from Google as well as Azure Functions. Eventually we went with AWS Lambdas.

                  PROS
                  • No servers to manage (obviously!)
                  • Limited fixed costs – you pay only for used time
                  • Automated scaling and balancing
                  • Automatic failover (or, at this level of abstraction, no failover problem at all)
                  • Security easier to provide and audit
                  • Low overhead at the start (with the certain level of knowledge)
                  • Short time to market
                  • Easy handover - deployment coupled with code
                  • Perfect choice for lean startups with fast-paced iterations
                  • Augmentation for the classic cloud, server(full) approach
                  CONS
                  • Not much know-how and best practices available about structuring the code and projects on the market
                  • Not suitable for complex business logic due to the risk of producing highly coupled code
                  • Cost difficult to estimate (helpful tools: serverlesscalc.com)
                  • Difficulty in migration to other platforms (Vendor lock⚠️)
                  • Little engineers with experience in serverless on the job market
                  • Steep learning curve for engineers without any cloud experience

                  More details are on our blog: https://evojam.com/blog/2018/12/5/should-you-go-serverless-meet-the-benefits-and-flaws-of-new-wave-of-cloud-solutions I hope it helps 🙌 & I'm curious of your experiences.

                  See more
                  Google Analytics logo

                  Google Analytics

                  112.1K
                  36.5K
                  5K
                  Enterprise-class web analytics.
                  112.1K
                  36.5K
                  + 1
                  5K
                  PROS OF GOOGLE ANALYTICS
                  • 1.5K
                    Free
                  • 925
                    Easy setup
                  • 888
                    Data visualization
                  • 696
                    Real-time stats
                  • 403
                    Comprehensive feature set
                  • 180
                    Goals tracking
                  • 154
                    Powerful funnel conversion reporting
                  • 137
                    Customizable reports
                  • 83
                    Custom events try
                  • 53
                    Elastic api
                  • 13
                    Updated regulary
                  • 8
                    Interactive Documentation
                  • 3
                    Google play
                  • 2
                    Advanced ecommerce
                  • 2
                    Industry Standard
                  • 2
                    Walkman music video playlist
                  • 1
                    Medium / Channel data split
                  • 1
                    Financial Management Challenges -2015h
                  • 1
                    Lifesaver
                  • 1
                    Easy to integrate
                  CONS OF GOOGLE ANALYTICS
                  • 9
                    Confusing UX/UI
                  • 6
                    Super complex
                  • 5
                    Very hard to build out funnels
                  • 3
                    Poor web performance metrics
                  • 2
                    Very easy to confuse the user of the analytics
                  • 2
                    Time spent on page isn't accurate out of the box

                  related Google Analytics posts

                  Tassanai Singprom

                  This is my stack in Application & Data

                  JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

                  My Utilities Tools

                  Google Analytics Postman Elasticsearch

                  My Devops Tools

                  Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

                  My Business Tools

                  Slack

                  See more
                  Max Musing
                  Founder & CEO at BaseDash · | 8 upvotes · 124.4K views

                  Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

                  Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

                  See more