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Dremio
Dremio

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Presto
Presto

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46
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Presto vs Dremio: What are the differences?

Developers describe Presto as "Distributed SQL Query Engine for Big Data". 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. On the other hand, Dremio is detailed as "Self-service data for everyone". It is a data-as-a-service platform that empowers users to discover, curate, accelerate, and share any data at any time, regardless of location, volume, or structure. Modern data is managed by a wide range of technologies, including relational databases, NoSQL datastores, file systems, Hadoop, and others.

Presto and Dremio belong to "Big Data Tools" category of the tech stack.

Presto is an open source tool with 9.47K GitHub stars and 3.22K GitHub forks. Here's a link to Presto's open source repository on GitHub.

- No public GitHub repository available -

What is Dremio?

It is a data-as-a-service platform that empowers users to discover, curate, accelerate, and share any data at any time, regardless of location, volume, or structure. Modern data is managed by a wide range of technologies, including relational databases, NoSQL datastores, file systems, Hadoop, and others.

What is 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.
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        What are some alternatives to Dremio and Presto?
        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
        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
        Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
        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 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.
        See all alternatives
        Decisions about Dremio and Presto
        StackShare Editors
        StackShare Editors
        Hadoop
        Hadoop
        Apache Spark
        Apache Spark
        Presto
        Presto

        Around 2015, the growing use of Uber’s data exposed limitations in the ETL and Vertica-centric setup, not to mention the increasing costs. “As our company grew, scaling our data warehouse became increasingly expensive. To cut down on costs, we started deleting older, obsolete data to free up space for new data.”

        To overcome these challenges, Uber rebuilt their big data platform around Hadoop. “More specifically, we introduced a Hadoop data lake where all raw data was ingested from different online data stores only once and with no transformation during ingestion.”

        “In order for users to access data in Hadoop, we introduced Presto to enable interactive ad hoc user queries, Apache Spark to facilitate programmatic access to raw data (in both SQL and non-SQL formats), and Apache Hive to serve as the workhorse for extremely large queries.

        See more
        StackShare Editors
        StackShare Editors
        Hadoop
        Hadoop
        Apache Spark
        Apache Spark
        Presto
        Presto

        To improve platform scalability and efficiency, Uber transitioned from JSON to Parquet, and built a central schema service to manage schemas and integrate different client libraries.

        While the first generation big data platform was vulnerable to upstream data format changes, “ad hoc data ingestions jobs were replaced with a standard platform to transfer all source data in its original, nested format into the Hadoop data lake.”

        These platform changes enabled the scaling challenges Uber was facing around that time: “On a daily basis, there were tens of terabytes of new data added to our data lake, and our Big Data platform grew to over 10,000 vcores with over 100,000 running batch jobs on any given day.”

        See more
        StackShare Editors
        StackShare Editors
        Kafka
        Kafka
        MySQL
        MySQL
        Scala
        Scala
        Apache Spark
        Apache Spark
        Presto
        Presto

        Slack’s data team works to “provide an ecosystem to help people in the company quickly and easily answer questions about usage, so they can make better and data informed decisions.” To achieve that goal, that rely on a complex data pipeline.

        An in-house tool call Sqooper scrapes MySQL backups and pipe them to S3. Job queue and log data is sent to Kafka then persisted to S3 using an open source tool called Secor, which was created by Pinterest.

        For compute, Amazon’s Elastic MapReduce (EMR) creates clusters preconfigured for Presto, Hive, and Spark.

        Presto is then used for ad-hoc questions, validating data assumptions, exploring smaller datasets, and creating visualizations for some internal tools. Hive is used for larger data sets or longer time series data, and Spark allows teams to write efficient and robust batch and aggregation jobs. Most of the Spark pipeline is written in Scala.

        Thrift binds all of these engines together with a typed schema and structured data.

        Finally, the Hive Metastore serves as the ground truth for all data and its schema.

        See more
        StackShare Editors
        StackShare Editors
        Prometheus
        Prometheus
        Chef
        Chef
        Consul
        Consul
        Memcached
        Memcached
        Hack
        Hack
        Swift
        Swift
        Hadoop
        Hadoop
        Terraform
        Terraform
        Airflow
        Airflow
        Apache Spark
        Apache Spark
        Kubernetes
        Kubernetes
        gRPC
        gRPC
        HHVM (HipHop Virtual Machine)
        HHVM (HipHop Virtual Machine)
        Presto
        Presto
        Kotlin
        Kotlin
        Apache Thrift
        Apache Thrift

        Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

        Apps
        • Web: a mix of JavaScript/ES6 and React.
        • Desktop: And Electron to ship it as a desktop application.
        • Android: a mix of Java and Kotlin.
        • iOS: written in a mix of Objective C and Swift.
        Backend
        • The core application and the API written in PHP/Hack that runs on HHVM.
        • The data is stored in MySQL using Vitess.
        • Caching is done using Memcached and MCRouter.
        • The search service takes help from SolrCloud, with various Java services.
        • The messaging system uses WebSockets with many services in Java and Go.
        • Load balancing is done using HAproxy with Consul for configuration.
        • Most services talk to each other over gRPC,
        • Some Thrift and JSON-over-HTTP
        • Voice and video calling service was built in Elixir.
        Data warehouse
        • Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
        Etc
        See more
        Eric Colson
        Eric Colson
        Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 355.4K views
        atStitch FixStitch Fix
        Kafka
        Kafka
        PostgreSQL
        PostgreSQL
        Amazon S3
        Amazon S3
        Apache Spark
        Apache Spark
        Presto
        Presto
        Python
        Python
        R
        R
        PyTorch
        PyTorch
        Docker
        Docker
        Amazon EC2 Container Service
        Amazon EC2 Container Service
        #AWS
        #Etl
        #ML
        #DataScience
        #DataStack
        #Data

        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
        Ashish Singh
        Ashish Singh
        Tech Lead, Big Data Platform at Pinterest · | 20 upvotes · 36.2K views
        Apache Hive
        Apache Hive
        Kubernetes
        Kubernetes
        Kafka
        Kafka
        Amazon S3
        Amazon S3
        Amazon EC2
        Amazon EC2
        Presto
        Presto
        #DataScience
        #DataEngineering
        #AWS
        #BigData

        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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