Alternatives to Dremio logo

Alternatives to Dremio

Presto, Apache Drill, Denodo, AtScale, and Snowflake are the most popular alternatives and competitors to Dremio.
59
174
+ 1
7

What is Dremio and what are its top alternatives?

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.
Dremio is a tool in the Big Data Tools category of a tech stack.
Dremio is an open source tool with GitHub stars and GitHub forks. Here’s a link to Dremio's open source repository on GitHub

Top Alternatives to Dremio

  • Presto

    Presto

    Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. ...

  • Apache Drill

    Apache Drill

    Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel. ...

  • Denodo

    Denodo

    It is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories. ...

  • AtScale

    AtScale

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

  • 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. ...

  • Segment

    Segment

    Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch. ...

  • 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. ...

  • 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. ...

Dremio alternatives & related posts

Presto logo

Presto

244
636
54
Distributed SQL Query Engine for Big Data
244
636
+ 1
54

related Presto posts

Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 34 upvotes · 587.6K 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

See more
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
Apache Drill logo

Apache Drill

53
110
14
Schema-Free SQL Query Engine for Hadoop and NoSQL
53
110
+ 1
14

related Apache Drill posts

Denodo logo

Denodo

15
60
0
Data virtualisation platform, allowing you to connect disparate data from any source
15
60
+ 1
0
PROS OF DENODO
    No pros available
    CONS OF DENODO
      No cons available

      related Denodo posts

      AtScale logo

      AtScale

      11
      38
      0
      The virtual data warehouse for the modern enterprise
      11
      38
      + 1
      0
      PROS OF ATSCALE
        No pros available
        CONS OF ATSCALE
          No cons available

          related AtScale posts

          Snowflake logo

          Snowflake

          401
          458
          5
          The data warehouse built for the cloud
          401
          458
          + 1
          5

          related Snowflake posts

          Shared insights
          on
          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

          related Segment posts

          Robert Zuber

          Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

          We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

          See more
          Yasmine de Aranda
          Chief Growth Officer at Huddol · | 6 upvotes · 16.2K views

          Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!

          See more
          Apache Spark logo

          Apache Spark

          2K
          2.1K
          127
          Fast and general engine for large-scale data processing
          2K
          2.1K
          + 1
          127

          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 · 817.6K 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
          Databricks logo

          Databricks

          169
          285
          0
          A unified analytics platform, powered by Apache Spark
          169
          285
          + 1
          0
          PROS OF DATABRICKS
            No pros available
            CONS OF DATABRICKS
              No cons available

              related Databricks posts