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Google Cloud Datastore
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Google Cloud Datastore vs Hadoop: What are the differences?

What is Google Cloud Datastore? A Fully Managed NoSQL Data Storage Service. Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.

What is Hadoop? Open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Google Cloud Datastore belongs to "NoSQL Database as a Service" category of the tech stack, while Hadoop can be primarily classified under "Databases".

"High scalability" is the top reason why over 4 developers like Google Cloud Datastore, while over 34 developers mention "Great ecosystem" as the leading cause for choosing Hadoop.

Hadoop is an open source tool with 9.27K GitHub stars and 5.78K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.

Airbnb, Uber Technologies, and Spotify are some of the popular companies that use Hadoop, whereas Google Cloud Datastore is used by Teleport, Policygenius, and Giftstarter. Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to Google Cloud Datastore, which is listed in 46 company stacks and 16 developer stacks.

- No public GitHub repository available -

What is Google Cloud Datastore?

Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.

What is Hadoop?

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
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Why do developers choose Google Cloud Datastore?
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      What tools integrate with Google Cloud Datastore?
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        What are some alternatives to Google Cloud Datastore and Hadoop?
        Amazon DynamoDB
        With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
        Redis
        Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
        MongoDB
        MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
        Elasticsearch
        Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
        Cassandra
        Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
        See all alternatives
        Decisions about Google Cloud Datastore and Hadoop
        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
        Interest over time
        Reviews of Google Cloud Datastore and Hadoop
        No reviews found
        How developers use Google Cloud Datastore and Hadoop
        Avatar of Pinterest
        Pinterest uses HadoopHadoop

        The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.

        Avatar of Yelp
        Yelp uses HadoopHadoop

        in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).

        Avatar of Pinterest
        Pinterest uses HadoopHadoop

        The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...

        Avatar of CommentBox.io
        CommentBox.io uses Google Cloud DatastoreGoogle Cloud Datastore

        This is our primary database, though most of our actual data is stored in static storage. This database houses the metadata necessary for indexing and finding static data.

        Avatar of papaver
        papaver uses Google Cloud DatastoreGoogle Cloud Datastore

        worked with a client that used datastore as their backend database. helped plan out their schema and architecture. loved the speed and simplicity.

        Avatar of Robert Brown
        Robert Brown uses HadoopHadoop

        Importing/Exporting data, interpreting results. Possible integration with SAS

        Avatar of Rohith Nandakumar
        Rohith Nandakumar uses HadoopHadoop

        TBD. Good to have I think. Analytics on loads of data, recommendations?

        Avatar of Giftstarter
        Giftstarter uses Google Cloud DatastoreGoogle Cloud Datastore

        We store our data in this store.

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