Alternatives to Denodo logo

Alternatives to Denodo

AtScale, Tableau, Presto, Snowflake, and Talend are the most popular alternatives and competitors to Denodo.
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What is Denodo and what are its top alternatives?

Denodo is a data virtualization tool that enables users to connect, discover, and access data across different sources without the need for data movement. Its key features include real-time data access, integration with various data sources, data lineage and governance, and advanced security measures. However, Denodo's pricing can be expensive for some organizations, and users may require specialized training to fully utilize its capabilities.

  1. Talend Data Fabric: Talend Data Fabric is a comprehensive data integration platform that includes capabilities for data virtualization. It offers features such as data quality management, data governance, and cloud integration. Pros include a user-friendly interface and extensive support for various data sources. However, it may not be as specialized in data virtualization as Denodo.
  2. Informatica Intelligent Data Platform: Informatica's platform offers data integration, data quality, and master data management solutions along with data virtualization capabilities. Key features include AI-driven data matching and data governance. Pros include strong data governance features, but it could be complex to set up and maintain compared to Denodo.
  3. WekaIO Matrix: WekaIO Matrix is a storage platform that provides data access and management capabilities for high-performance computing environments. It offers scalable storage solutions with data virtualization features. Pros include high-performance data processing and scalability, but it may not have the same breadth of data sources supported as Denodo.
  4. AtScale: AtScale is a data virtualization platform that focuses on providing a single view of data across multiple sources. It offers features such as intelligent data caching and automated data governance. Pros include ease of use and quick deployment, but it may lack some advanced features compared to Denodo.
  5. Starburst Data: Starburst Data provides a data virtualization platform based on open-source technology. It offers features such as query acceleration and federated queries across different data sources. Pros include cost-effectiveness and community support, but it may require more technical expertise to manage compared to Denodo.
  6. CData Software: CData Software offers connectivity solutions for data virtualization, enabling users to connect to various data sources through standard interfaces. Key features include automatic schema discovery and data manipulation capabilities. Pros include ease of integration with existing systems, but it may not have as robust data governance features as Denodo.
  7. Dremio: Dremio is a data virtualization and data lake platform that focuses on self-service data access and analytics. It offers features such as SQL querying and data acceleration. Pros include high performance and scalability, but it may not have the same level of data governance as Denodo.
  8. Rockset: Rockset is a real-time indexing and analytics database platform that offers data virtualization capabilities. It enables users to query and analyze data across different sources in real-time. Pros include real-time data processing and scalability, but it may not have as extensive connectivity options as Denodo.
  9. Zaloni Arena: Zaloni Arena is a data management platform that includes data virtualization features for creating a unified view of data. It offers data cataloging, data governance, and data integration capabilities. Pros include comprehensive data management features, but it may not be as focused on data virtualization as Denodo.
  10. HotTopics: HotTopics is an open-source data virtualization tool that provides a lightweight and flexible solution for connecting to different data sources. It offers features such as data transformation and data modeling. Pros include cost-effectiveness and customization options, but it may not have as extensive support and documentation as Denodo.

Top Alternatives to Denodo

  • AtScale
    AtScale

    Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics. ...

  • Tableau
    Tableau

    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...

  • Presto
    Presto

    Distributed SQL Query Engine for Big Data

  • 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. ...

  • Talend
    Talend

    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms. ...

  • 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. ...

  • Google Tag Manager
    Google Tag Manager

    Tag Manager gives you the ability to add and update your own tags for conversion tracking, site analytics, remarketing, and more. There are nearly endless ways to track user behavior across your sites and apps, and the intuitive design lets you change tags whenever you want. ...

  • Mixpanel
    Mixpanel

    Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience. ...

Denodo alternatives & related posts

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      Hello everyone,

      My team and I are currently in the process of selecting a Business Intelligence (BI) tool for our actively developing company, which has over 500 employees. We are considering open-source options.

      We are keen to connect with a Head of Analytics or BI Analytics professional who has extensive experience working with any of these systems and is willing to share their insights. Ideally, we would like to speak with someone from companies that have transitioned from proprietary BI tools (such as PowerBI, Qlik, or Tableau) to open-source BI tools, or vice versa.

      If you have any contacts or recommendations for individuals we could reach out to regarding this matter, we would greatly appreciate it. Additionally, if you are personally willing to share your experiences, please feel free to reach out to me directly. Thank you!

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      Presto logo

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        Ashish Singh
        Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3.4M views

        To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

        Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

        We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

        Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

        Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

        #BigData #AWS #DataScience #DataEngineering

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        Eric Colson
        Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 6.2M 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.

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        #DataScience #DataStack #Data

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              Max Musing
              Founder & CEO at BaseDash · | 9 upvotes · 378.9K 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.

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