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AWS Glue
AWS Glue

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Presto

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AWS Glue vs Presto: What are the differences?

What is AWS Glue? Fully managed extract, transform, and load (ETL) service. A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.

What is Presto? 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.

AWS Glue and Presto can be primarily classified as "Big Data" tools.

Presto is an open source tool with 9.29K GitHub stars and 3.15K GitHub forks. Here's a link to Presto's open source repository on GitHub.

According to the StackShare community, Presto has a broader approval, being mentioned in 19 company stacks & 11 developers stacks; compared to AWS Glue, which is listed in 13 company stacks and 7 developer stacks.

- No public GitHub repository available -

What is AWS Glue?

A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.

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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Why do developers choose AWS Glue?
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        What are some alternatives to AWS Glue and Presto?
        AWS Data Pipeline
        AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.
        Airflow
        Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
        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.
        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.
        Alooma
        Get the power of big data in minutes with Alooma and Amazon Redshift. Simply build your pipelines and map your events using Alooma’s friendly mapping interface. Query, analyze, visualize, and predict now.
        See all alternatives
        Decisions about AWS Glue and Presto
        StackShare Editors
        StackShare Editors
        Presto
        Presto
        Apache Spark
        Apache Spark
        Hadoop
        Hadoop

        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
        Presto
        Presto
        Apache Spark
        Apache Spark
        Hadoop
        Hadoop

        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.”

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

        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.

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

        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 · 267.4K views
        atStitch FixStitch Fix
        Amazon EC2 Container Service
        Amazon EC2 Container Service
        Docker
        Docker
        PyTorch
        PyTorch
        R
        R
        Python
        Python
        Presto
        Presto
        Apache Spark
        Apache Spark
        Amazon S3
        Amazon S3
        PostgreSQL
        PostgreSQL
        Kafka
        Kafka
        #Data
        #DataStack
        #DataScience
        #ML
        #Etl
        #AWS

        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

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