Alternatives to Cassandra logo

Alternatives to Cassandra

HBase, Google Cloud Bigtable, Hadoop, Redis, and Couchbase are the most popular alternatives and competitors to Cassandra.
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What is Cassandra and what are its top alternatives?

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.
Cassandra is a tool in the Databases category of a tech stack.
Cassandra is an open source tool with 8.6K GitHub stars and 3.6K GitHub forks. Here’s a link to Cassandra's open source repository on GitHub

Top Alternatives to Cassandra

  • HBase
    HBase

    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop. ...

  • Google Cloud Bigtable
    Google Cloud Bigtable

    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail. ...

  • Hadoop
    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. ...

  • Redis
    Redis

    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. ...

  • Couchbase
    Couchbase

    Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands. ...

  • MySQL
    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

  • PostgreSQL
    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

  • Oracle
    Oracle

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...

Cassandra alternatives & related posts

HBase logo

HBase

455
492
15
The Hadoop database, a distributed, scalable, big data store
455
492
+ 1
15
PROS OF HBASE
  • 9
    Performance
  • 5
    OLTP
  • 1
    Fast Point Queries
CONS OF HBASE
    Be the first to leave a con

    related HBase posts

    I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

    See more

    Hi, I'm building a machine learning pipelines to store image bytes and image vectors in the backend.

    So, when users query for the random access image data (key), we return the image bytes and perform machine learning model operations on it.

    I'm currently considering going with Amazon S3 (in the future, maybe add Redis caching layer) as the backend system to store the information (s3 buckets with sharded prefixes).

    As the latency of S3 is 100-200ms (get/put) and it has a high throughput of 3500 puts/sec and 5500 gets/sec for a given bucker/prefix. In the future I need to reduce the latency, I can add Redis cache.

    Also, s3 costs are way fewer than HBase (on Amazon EC2 instances with 3x replication factor)

    I have not personally used HBase before, so can someone help me if I'm making the right choice here? I'm not aware of Hbase latencies and I have learned that the MOB feature on Hbase has to be turned on if we have store image bytes on of the column families as the avg image bytes are 240Kb.

    See more
    Google Cloud Bigtable logo

    Google Cloud Bigtable

    138
    362
    25
    The same database that powers Google Search, Gmail and Analytics
    138
    362
    + 1
    25
    PROS OF GOOGLE CLOUD BIGTABLE
    • 11
      High performance
    • 9
      Fully managed
    • 5
      High scalability
    CONS OF GOOGLE CLOUD BIGTABLE
      Be the first to leave a con

      related Google Cloud Bigtable posts

      Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

      Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

      Check Out My Architecture: CLICK ME

      Check out the GitHub repo attached

      See more
      Rory Gwozdz
      CTO at Harvested Financial · | 2 upvotes · 124.7K views

      I'm trying to build a way to read financial data really, really fast, for low cost. We are write/update-light (in this arena) and read-heavy. Google BigQuery being serverless can keep costs beyond low, but query speeds are always a few seconds because, I think, of the lack of indexing and potential to take advantage of the structure of the common queries. I have tried various partitions on BigQuery to speed things up too with some success but nothing extraordinary. I have never used Google Cloud Bigtable but get how it works conceptually. I believe it would make date-range based queries markedly faster. Question is, are there ways to take advantage of date-ranges in BigQuery, or does it makes sense to just shift to BigTable for mega-fast reads? I'd love to get sub-50ms.

      See more
      Hadoop logo

      Hadoop

      2.5K
      2.3K
      56
      Open-source software for reliable, scalable, distributed computing
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      PROS OF HADOOP
      • 39
        Great ecosystem
      • 11
        One stack to rule them all
      • 4
        Great load balancer
      • 1
        Amazon aws
      • 1
        Java syntax
      CONS OF HADOOP
        Be the first to leave a con

        related Hadoop posts

        Shared insights
        on
        KafkaKafkaHadoopHadoop
        at

        The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

        For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

        Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

        See more
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 3M 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
        Redis logo

        Redis

        58.5K
        45.1K
        3.9K
        Open source (BSD licensed), in-memory data structure store
        58.5K
        45.1K
        + 1
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        PROS OF REDIS
        • 886
          Performance
        • 542
          Super fast
        • 513
          Ease of use
        • 444
          In-memory cache
        • 324
          Advanced key-value cache
        • 194
          Open source
        • 182
          Easy to deploy
        • 164
          Stable
        • 155
          Free
        • 121
          Fast
        • 42
          High-Performance
        • 40
          High Availability
        • 35
          Data Structures
        • 32
          Very Scalable
        • 24
          Replication
        • 22
          Great community
        • 22
          Pub/Sub
        • 19
          "NoSQL" key-value data store
        • 16
          Hashes
        • 13
          Sets
        • 11
          Sorted Sets
        • 10
          NoSQL
        • 10
          Lists
        • 9
          Async replication
        • 9
          BSD licensed
        • 8
          Bitmaps
        • 8
          Integrates super easy with Sidekiq for Rails background
        • 7
          Keys with a limited time-to-live
        • 7
          Open Source
        • 6
          Lua scripting
        • 6
          Strings
        • 5
          Awesomeness for Free
        • 5
          Hyperloglogs
        • 4
          Transactions
        • 4
          Outstanding performance
        • 4
          Runs server side LUA
        • 4
          LRU eviction of keys
        • 4
          Feature Rich
        • 4
          Written in ANSI C
        • 4
          Networked
        • 3
          Data structure server
        • 3
          Performance & ease of use
        • 2
          Dont save data if no subscribers are found
        • 2
          Automatic failover
        • 2
          Easy to use
        • 2
          Temporarily kept on disk
        • 2
          Scalable
        • 2
          Existing Laravel Integration
        • 2
          Channels concept
        • 2
          Object [key/value] size each 500 MB
        • 2
          Simple
        CONS OF REDIS
        • 15
          Cannot query objects directly
        • 3
          No secondary indexes for non-numeric data types
        • 1
          No WAL

        related Redis posts

        Russel Werner
        Lead Engineer at StackShare · | 32 upvotes · 2.5M views

        StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

        Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

        #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

        See more
        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M views

        Our whole DevOps stack consists of the following tools:

        • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
        • Respectively Git as revision control system
        • SourceTree as Git GUI
        • Visual Studio Code as IDE
        • CircleCI for continuous integration (automatize development process)
        • Prettier / TSLint / ESLint as code linter
        • SonarQube as quality gate
        • Docker as container management (incl. Docker Compose for multi-container application management)
        • VirtualBox for operating system simulation tests
        • Kubernetes as cluster management for docker containers
        • Heroku for deploying in test environments
        • nginx as web server (preferably used as facade server in production environment)
        • SSLMate (using OpenSSL) for certificate management
        • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
        • PostgreSQL as preferred database system
        • Redis as preferred in-memory database/store (great for caching)

        The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

        • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
        • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
        • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
        • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
        • Scalability: All-in-one framework for distributed systems.
        • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
        See more
        Couchbase logo

        Couchbase

        474
        601
        110
        Document-Oriented NoSQL Database
        474
        601
        + 1
        110
        PROS OF COUCHBASE
        • 18
          High performance
        • 18
          Flexible data model, easy scalability, extremely fast
        • 9
          Mobile app support
        • 7
          You can query it with Ansi-92 SQL
        • 6
          All nodes can be read/write
        • 5
          Equal nodes in cluster, allowing fast, flexible changes
        • 5
          Both a key-value store and document (JSON) db
        • 5
          Open source, community and enterprise editions
        • 4
          Automatic configuration of sharding
        • 4
          Local cache capability
        • 3
          Easy setup
        • 3
          Linearly scalable, useful to large number of tps
        • 3
          Easy cluster administration
        • 3
          Cross data center replication
        • 3
          SDKs in popular programming languages
        • 3
          Elasticsearch connector
        • 3
          Web based management, query and monitoring panel
        • 2
          Map reduce views
        • 2
          DBaaS available
        • 2
          NoSQL
        • 1
          Buckets, Scopes, Collections & Documents
        • 1
          FTS + SQL together
        CONS OF COUCHBASE
        • 3
          Terrible query language

        related Couchbase posts

        Gabriel Pa

        We implemented our first large scale EPR application from naologic.com using CouchDB .

        Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

        It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

        See more
        Ilias Mentzelos
        Software Engineer at Plum Fintech · | 9 upvotes · 233.5K views
        Shared insights
        on
        MongoDBMongoDBCouchbaseCouchbase

        Hey, we want to build a referral campaign mechanism that will probably contain millions of records within the next few years. We want fast read access based on IDs or some indexes, and isolation is crucial as some listeners will try to update the same document at the same time. What's your suggestion between Couchbase and MongoDB? Thanks!

        See more
        MySQL logo

        MySQL

        123.2K
        104.2K
        3.7K
        The world's most popular open source database
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        3.7K
        PROS OF MYSQL
        • 800
          Sql
        • 679
          Free
        • 562
          Easy
        • 528
          Widely used
        • 489
          Open source
        • 180
          High availability
        • 160
          Cross-platform support
        • 104
          Great community
        • 78
          Secure
        • 75
          Full-text indexing and searching
        • 25
          Fast, open, available
        • 16
          SSL support
        • 15
          Reliable
        • 14
          Robust
        • 8
          Enterprise Version
        • 7
          Easy to set up on all platforms
        • 2
          NoSQL access to JSON data type
        • 1
          Relational database
        • 1
          Easy, light, scalable
        • 1
          Sequel Pro (best SQL GUI)
        • 1
          Replica Support
        CONS OF MYSQL
        • 16
          Owned by a company with their own agenda
        • 3
          Can't roll back schema changes

        related MySQL posts

        Nick Rockwell
        SVP, Engineering at Fastly · | 46 upvotes · 3.5M views

        When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

        So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

        React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

        Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

        See more
        Tim Abbott

        We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

        We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

        And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

        I can't recommend it highly enough.

        See more
        PostgreSQL logo

        PostgreSQL

        96.5K
        80.8K
        3.5K
        A powerful, open source object-relational database system
        96.5K
        80.8K
        + 1
        3.5K
        PROS OF POSTGRESQL
        • 762
          Relational database
        • 510
          High availability
        • 439
          Enterprise class database
        • 383
          Sql
        • 304
          Sql + nosql
        • 173
          Great community
        • 147
          Easy to setup
        • 131
          Heroku
        • 130
          Secure by default
        • 113
          Postgis
        • 50
          Supports Key-Value
        • 48
          Great JSON support
        • 34
          Cross platform
        • 32
          Extensible
        • 28
          Replication
        • 26
          Triggers
        • 23
          Rollback
        • 22
          Multiversion concurrency control
        • 21
          Open source
        • 18
          Heroku Add-on
        • 17
          Stable, Simple and Good Performance
        • 15
          Powerful
        • 13
          Lets be serious, what other SQL DB would you go for?
        • 11
          Good documentation
        • 8
          Intelligent optimizer
        • 8
          Free
        • 8
          Scalable
        • 8
          Reliable
        • 7
          Transactional DDL
        • 7
          Modern
        • 6
          One stop solution for all things sql no matter the os
        • 5
          Relational database with MVCC
        • 5
          Faster Development
        • 4
          Developer friendly
        • 4
          Full-Text Search
        • 3
          Free version
        • 3
          Great DB for Transactional system or Application
        • 3
          Relational datanbase
        • 3
          search
        • 3
          Open-source
        • 3
          Excellent source code
        • 2
          Full-text
        • 2
          Text
        • 0
          Native
        CONS OF POSTGRESQL
        • 10
          Table/index bloatings

        related PostgreSQL posts

        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M views

        Our whole DevOps stack consists of the following tools:

        • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
        • Respectively Git as revision control system
        • SourceTree as Git GUI
        • Visual Studio Code as IDE
        • CircleCI for continuous integration (automatize development process)
        • Prettier / TSLint / ESLint as code linter
        • SonarQube as quality gate
        • Docker as container management (incl. Docker Compose for multi-container application management)
        • VirtualBox for operating system simulation tests
        • Kubernetes as cluster management for docker containers
        • Heroku for deploying in test environments
        • nginx as web server (preferably used as facade server in production environment)
        • SSLMate (using OpenSSL) for certificate management
        • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
        • PostgreSQL as preferred database system
        • Redis as preferred in-memory database/store (great for caching)

        The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

        • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
        • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
        • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
        • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
        • Scalability: All-in-one framework for distributed systems.
        • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
        See more
        Jeyabalaji Subramanian

        Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

        We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

        Based on the above criteria, we selected the following tools to perform the end to end data replication:

        We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

        We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

        In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

        Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

        In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

        See more
        Oracle logo

        Oracle

        2.3K
        1.7K
        113
        An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
        2.3K
        1.7K
        + 1
        113
        PROS OF ORACLE
        • 44
          Reliable
        • 33
          Enterprise
        • 15
          High Availability
        • 5
          Hard to maintain
        • 5
          Expensive
        • 4
          Maintainable
        • 4
          Hard to use
        • 3
          High complexity
        CONS OF ORACLE
        • 14
          Expensive

        related Oracle posts

        Saurav Pandit
        Application Devloper at Bny Mellon · | 9 upvotes · 273.6K views

        I have just started learning Python 3 week back. I want to create REST api using python. The api will be use to save form data in Oracle database. The front end is using AngularJS 8 with Angular Material. In python there are so many framework for developing REST ** I am looking for some suggestions which REST framework to choose? ** Here are some feature I am looking for * Easy integration and unit testing like in Angular we just run command. * Code packageing, like in Java maven project we can build and package. I am looking for something which I can push in artifactory and deploy whole code as package. *Support for swagger/ OpenAPI * Support for JSON Web Token * Support for testcase coverage report Framework can have feature included or can be available by extension.

        See more
        Dishi Jain
        Shared insights
        on
        OracleOracleKubernetesKubernetes

        So we are re-engineering our application database to make it cloud-native and deploy on the Kubernetes platform. Currently, our data lies on the Oracle 19c database and it is normalized extensively. We store pdfs, txt files and allow a user to edit, delete, view, create new transactions. Now I want to pick a DB, which makes the re-engineering, not a big deal but allows us to store data in a distributed manner on Kubernetes. Please assist me.

        See more