Alternatives to Qubole logo

Alternatives to Qubole

Databricks, Snowflake, Google BigQuery, Amazon Redshift, and Amazon EMR are the most popular alternatives and competitors to Qubole.
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What is Qubole and what are its top alternatives?

Qubole is a cloud based service that makes big data easy for analysts and data engineers.
Qubole is a tool in the Big Data as a Service category of a tech stack.

Top Alternatives to Qubole

  • 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. ...

  • 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. ...

  • Google BigQuery
    Google BigQuery

    Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python. ...

  • 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. ...

  • 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. ...

  • Stitch
    Stitch

    Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company. ...

  • Cloudera Enterprise
    Cloudera Enterprise

    Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts. ...

  • Dremio
    Dremio

    Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts. ...

Qubole 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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    Best Performances on large datasets
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    True lakehouse architecture
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    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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    Jan Vlnas
    Developer Advocate at Superface · | 5 upvotes · 29.1K views

    From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

    I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

    Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

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    Snowflake logo

    Snowflake

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    The data warehouse built for the cloud
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    PROS OF SNOWFLAKE
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      Public and Private Data Sharing
    • 3
      Good Performance
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      Multicloud
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      Great Documentation
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      User Friendly
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      Serverless
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      Innovative
    • 1
      Economical
    • 1
      Usage based billing
    CONS OF SNOWFLAKE
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      related Snowflake posts

      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

      Please feel free to drop some knowledge/wish list stuff on me for a better pipeline here!

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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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      Google BigQuery logo

      Google BigQuery

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      Analyze terabytes of data in seconds
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      PROS OF GOOGLE BIGQUERY
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        High Performance
      • 24
        Easy to use
      • 21
        Fully managed service
      • 19
        Cheap Pricing
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        Process hundreds of GB in seconds
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        Full table scans in seconds, no indexes needed
      • 11
        Big Data
      • 8
        Always on, no per-hour costs
      • 6
        Good combination with fluentd
      • 4
        Machine learning
      CONS OF GOOGLE BIGQUERY
      • 1
        You can't unit test changes in BQ data

      related Google BigQuery posts

      Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

      Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

      Check Out My Architecture: CLICK ME

      Check out the GitHub repo attached

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      Tim Specht
      ‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 697.6K views

      In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.

      While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.

      In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.

      #ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService

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      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
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        Data Warehousing
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        Scalable
      • 17
        SQL
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        Backed by Amazon
      • 5
        Encryption
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        Cheap and reliable
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        Isolation
      • 1
        Best Cloud DW Performance
      • 1
        Fast columnar storage
      CONS OF AMAZON REDSHIFT
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        Julien DeFrance
        Principal Software Engineer at Tophatter · | 16 upvotes · 2.6M 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
        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
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          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
          Massive data handling
        • 1
          Great support
        CONS OF AMAZON EMR
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          related Amazon EMR posts

          Stitch logo

          Stitch

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          All your data. In your data warehouse. In minutes.
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          PROS OF STITCH
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            3 minutes to set up
          • 4
            Super simple, great support
          CONS OF STITCH
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            related Stitch posts

            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
            Cyril Duchon-Doris

            Hello, For security and strategic reasons, we are migrating our apps from AWS/Google to a cloud provider with more security certifications and fewer functionalities, named Outscale. So far we have been using Google BigQuery as our data warehouse with ELT workflows (using Stitch and dbt ) and we need to migrate our data ecosystem to this new cloud provider.

            We are setting up a Kubernetes cluster in our new cloud provider for our apps. Regarding the data warehouse, it's not clear if there are advantages/inconvenients about setting it up on kubernetes (apart from having to create node groups and tolerations with more ram/cpu). Also, we are not sure what's the best Open source or on-premise tool to use. The main requirement is that data must remain in the secure cluster, and no external entity (especially US) can have access to it. We have a dev cluster/environment and a production cluster/environment on this cloud.

            Regarding the actual DWH usage - Today we have ~1.5TB in BigQuery in production. We're going to run our initial rests with ~50-100GB of data for our test cluster - Most of our data comes from other databases, so in most cases, we already have replicated sources somewhere, and there are only a handful of collections whose source is directly in the DWH (such as snapshots, some external data we've fetched at some point, google analytics, etc) and needs appropriate level of replication - We are a team of 30-ish people, we do not have critical needs regarding analytics speed, and we do not need real time. We rebuild our DBT models 2-3 times a day and this usually proves enough

            Apart from postgreSQL, I haven't really found open-source or on-premise alternatives for setting up a data warehouse, and running transformations with DBT. There is also the question of data ingestion, I've selected Airbyte and @meltano and I have troubles understanding if one of the 2 is better but Airbytes seems to have a bigger community.

            What do you suggest regarding the data warehouse, and the ELT workflows ? - Kubernetes or not kubernetes ? - Postgresql or something else ? if postgre, what are the important configs you'd have in mind ? - Airbyte/DBT or something else.

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            Cloudera Enterprise logo

            Cloudera Enterprise

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            Enterprise Platform for Big Data
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            PROS OF CLOUDERA ENTERPRISE
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              CONS OF CLOUDERA ENTERPRISE
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                Dremio logo

                Dremio

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                The data lake engine
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                PROS OF DREMIO
                • 3
                  Nice GUI to enable more people to work with Data
                • 2
                  Connect NoSQL databases with RDBMS
                • 2
                  Easier to Deploy
                • 1
                  Free
                CONS OF DREMIO
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