Google Cloud Storage vs Presto

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Google Cloud Storage
Google Cloud Storage

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Google Cloud Storage vs Presto: What are the differences?

Developers describe Google Cloud Storage as "Durable and highly available object storage service". Google Cloud Storage allows world-wide storing and retrieval of any amount of data and at any time. It provides a simple programming interface which enables developers to take advantage of Google's own reliable and fast networking infrastructure to perform data operations in a secure and cost effective manner. If expansion needs arise, developers can benefit from the scalability provided by Google's infrastructure. On the other hand, Presto is detailed 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.

Google Cloud Storage belongs to "Cloud Storage" category of the tech stack, while Presto can be primarily classified under "Big Data Tools".

"Scalable" is the primary reason why developers consider Google Cloud Storage over the competitors, whereas "Works directly on files in s3 (no ETL)" was stated as the key factor in picking Presto.

Presto is an open source tool with 9.22K GitHub stars and 3.12K GitHub forks. Here's a link to Presto's open source repository on GitHub.

Evernote, Bugsnag, and Wix are some of the popular companies that use Google Cloud Storage, whereas Presto is used by Airbnb, Facebook, and Netflix. Google Cloud Storage has a broader approval, being mentioned in 179 company stacks & 74 developers stacks; compared to Presto, which is listed in 19 company stacks and 11 developer stacks.

- No public GitHub repository available -

What is Google Cloud Storage?

Google Cloud Storage allows world-wide storing and retrieval of any amount of data and at any time. It provides a simple programming interface which enables developers to take advantage of Google's own reliable and fast networking infrastructure to perform data operations in a secure and cost effective manner. If expansion needs arise, developers can benefit from the scalability provided by Google's infrastructure.

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 Google Cloud Storage?
Why do developers choose Presto?

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      What are some alternatives to Google Cloud Storage and Presto?
      Google Drive
      The Drive SDK gives you a group of APIs along with client libraries, language-specific examples, and documentation to help you develop apps that integrate with Drive. The core functionality of Drive apps is to download and upload files in Google Drive. However, the Drive SDK provides a lot more than just storage.
      Firebase
      Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds.
      Amazon Glacier
      In order to keep costs low, Amazon Glacier is optimized for data that is infrequently accessed and for which retrieval times of several hours are suitable. With Amazon Glacier, customers can reliably store large or small amounts of data for as little as $0.01 per gigabyte per month, a significant savings compared to on-premises solutions.
      Amazon S3
      Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
      DigitalOcean Spaces
      DigitalOcean Spaces are designed to make it easy and cost effective to store and serve massive amounts of data. Spaces are ideal for storing static, unstructured data like audio, video, and images as well as large amounts of text.
      See all alternatives
      Decisions about Google Cloud Storage 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.”

      See more
      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 · 260.4K 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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      Reviews of Google Cloud Storage and Presto
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      How developers use Google Cloud Storage and Presto
      Avatar of itzMe
      itzMe uses Google Cloud StorageGoogle Cloud Storage

      Amazon / Google... Google / Amazon ... we decided to take the plunge and go for Google Cloud services as their services seem to be a bit more thought through and structured as they have not developed so organically.

      Avatar of Matt Welke
      Matt Welke uses Google Cloud StorageGoogle Cloud Storage

      When creating proofs of concept or small personal projects that are hosted primarily in GCP, this is the object storage service I usually pair them with.

      Avatar of Flutter Health Inc.
      Flutter Health Inc. uses Google Cloud StorageGoogle Cloud Storage

      We use Google Cloud Storage to store the images and other files that are added (uploaded) or generated in the Flutter application.

      Avatar of CommentBox.io
      CommentBox.io uses Google Cloud StorageGoogle Cloud Storage

      All comments, votes, and other actions live here as a highly-scalable, reliable, multi-region storage solution.

      Avatar of Cirrus Labs
      Cirrus Labs uses Google Cloud StorageGoogle Cloud Storage

      Cirrus CI can store build artifacts in Google Cloud Storage

      How much does Google Cloud Storage cost?
      How much does Presto cost?
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