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.
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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

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A unified analytics platform, powered by Apache Spark
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PROS OF DATABRICKS
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    CONS OF DATABRICKS
      No cons available

      related Databricks posts

      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
          No cons available

          related Azure Machine Learning posts

          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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            CONS OF AZURE HDINSIGHT
              No cons available

              related Azure HDInsight posts

              Apache Spark logo

              Apache Spark

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              Fast and general engine for large-scale data processing
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              related Apache Spark posts

              Eric Colson
              Chief Algorithms Officer at Stitch Fix · | 20 upvotes · 1.6M 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 · 814.2K 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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              Snowflake logo

              Snowflake

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              The data warehouse built for the cloud
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              Shared insights
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              Google BigQuery
              Snowflake

              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
              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 · 47.4K 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

                  related Azure Functions posts

                  Kestas Barzdaitis
                  Entrepreneur & Engineer · | 16 upvotes · 363.6K 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

                  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

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                  Yonas Beshawred

                  Adopting Amplitude was one of the best decisions we've made. We didn't try any of the alternatives- the free tier was really generous so it was easy to justify trying it out (via Segment). We've had Google Analytics since inception, but just for logged out traffic. We knew we'd need some sort of #FunnelAnalysisAnalytics solution, so it came down to just a few solutions.

                  We had heard good things about Amplitude from friends and even had a consultant/advisor who was an Amplitude pro from using it as his company, so he kinda convinced us to splurge on the Enterprise tier for the behavioral cohorts alone. Writing the queries they provide via a few clicks in their UI would take days/weeks to craft in SQL. The behavioral cohorts allow us to create a lot of useful retention charts.

                  Another really useful feature is kinda minor but kinda not. When you change a saved chart, a new URL gets generated and is visible in your browser (chartURL/edit) and that URL is immediately available to share with your team. It may sound inconsequential, but in practice, it makes it really easy to share and iterate on graphs. Only complaint is that you have to explicitly tag other team members as owners of whatever chart you're creating for them to be able to edit it and save it. I can see why this is the case, but more often than not, the people I'm sharing the chart with are the ones I want to edit it 🤷🏾‍♂️

                  The Engagement Matrix feature is also really helpful (once you filter out the noisy events). Charts and dashboards are also great and make it easy for us to focus on the important metrics. We've been using Amplitude in production for about 6 months now. There's a bunch of other features we don't use regularly like Pathfinder, etc that I personally don't fully understand yet but I'm sure we'll start using them eventually.

                  Again, haven't tried any of the alternatives like Heap, Mixpanel, or Kissmetrics so can't speak to those, but Amplitude works great for us.

                  #analytics analyticsstack

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