Alternatives to Apache Parquet logo

Alternatives to Apache Parquet

Avro, Apache Kudu, JSON, Cassandra, and HBase are the most popular alternatives and competitors to Apache Parquet.
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What is Apache Parquet and what are its top alternatives?

It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.
Apache Parquet is a tool in the Databases category of a tech stack.
Apache Parquet is an open source tool with 1.5K GitHub stars and 1.2K GitHub forks. Here’s a link to Apache Parquet's open source repository on GitHub

Top Alternatives to Apache Parquet

  • Avro
    Avro

    It is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format. ...

  • Apache Kudu
    Apache Kudu

    A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data. ...

  • JSON
    JSON

    JavaScript Object Notation is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language. ...

  • Cassandra
    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. ...

  • 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. ...

  • 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. ...

  • MongoDB
    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. ...

Apache Parquet alternatives & related posts

Avro logo

Avro

168
154
0
A data serialization framework
168
154
+ 1
0
PROS OF AVRO
    Be the first to leave a pro
    CONS OF AVRO
      Be the first to leave a con

      related Avro posts

      Apache Kudu logo

      Apache Kudu

      64
      225
      10
      Fast Analytics on Fast Data. A columnar storage manager developed for the Hadoop platform
      64
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      10
      PROS OF APACHE KUDU
      • 10
        Realtime Analytics
      CONS OF APACHE KUDU
      • 0
        Restart time

      related Apache Kudu posts

      I have been working on a Java application to demonstrate the latency for the select/insert/update operations on KUDU storage using Apache Kudu API - Java based client. I have a few queries about using Apache Kudu API

      1. Do we have JDBC wrapper to use Apache Kudu API for getting connection to Kudu masters with connection pool mechanism and all DB operations?

      2. Does Apache KuduAPI supports order by, group by, and aggregate functions? if yes, how to implement these functions using Kudu APIs.

      3. How can we add kudu predicates to Kudu update operation? if yes, how?

      4. Does Apache Kudu API supports batch insertion (execute the Kudu Insert for multiple rows at one go instead of row by row)? (like Kudusession.apply(List);)

      5. Does Apache Kudu API support join on tables?

      6. which tool is preferred over others (Apache Impala /Kudu API) for read and update/insert DB operations?

      See more
      JSON logo

      JSON

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      8
      A lightweight data-interchange format
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      PROS OF JSON
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        Widely supported
      • 4
        Simple
      CONS OF JSON
        Be the first to leave a con

        related JSON posts

        Ali Soueidan
        Creative Web Developer at Ali Soueidan · | 18 upvotes · 871.7K views

        Application and Data: Since my personal website ( https://alisoueidan.com ) is a SPA I've chosen to use Vue.js, as a framework to create it. After a short skeptical phase I immediately felt in love with the single file component concept! I also used vuex for state management, which makes working with several components, which are communicating with each other even more fun and convenient to use. Of course, using Vue requires using JavaScript as well, since it is the basis of it.

        For markup and style, I used Pug and Sass, since they’re the perfect match to me. I love the clean and strict syntax of both of them and even more that their structure is almost similar. Also, both of them come with an expanded functionality such as mixins, loops and so on related to their “siblings” (HTML and CSS). Both of them require nesting and prevent untidy code, which can be a huge advantage when working in teams. I used JSON to store data (since the data quantity on my website is moderate) – JSON works also good in combo with Pug, using for loops, based on the JSON Objects for example.

        To send my contact form I used PHP, since sending emails using PHP is still relatively convenient, simple and easy done.

        DevOps: Of course, I used Git to do my version management (which I even do in smaller projects like my website just have an additional backup of my code). On top of that I used GitHub since it now supports private repository for free accounts (which I am using for my own). I use Babel to use ES6 functionality such as arrow functions and so on, and still don’t losing cross browser compatibility.

        Side note: I used npm for package management. 🎉

        *Business Tools: * I use Asana to organize my project. This is a big advantage to me, even if I work alone, since “private” projects can get interrupted for some time. By using Asana I still know (even after month of not touching a project) what I’ve done, on which task I was at last working on and what still is to do. Working in Teams (for enterprise I’d take on Jira instead) of course Asana is a Tool which I really love to use as well. All the graphics on my website are SVG which I have created with Adobe Illustrator and adjusted within the SVG code or by using JavaScript or CSS (SASS).

        See more

        I use Visual Studio Code because at this time is a mature software and I can do practically everything using it.

        • It's free and open source: The project is hosted on GitHub and it’s free to download, fork, modify and contribute to the project.

        • Multi-platform: You can download binaries for different platforms, included Windows (x64), MacOS and Linux (.rpm and .deb packages)

        • LightWeight: It runs smoothly in different devices. It has an average memory and CPU usage. Starts almost immediately and it’s very stable.

        • Extended language support: Supports by default the majority of the most used languages and syntax like JavaScript, HTML, C#, Swift, Java, PHP, Python and others. Also, VS Code supports different file types associated to projects like .ini, .properties, XML and JSON files.

        • Integrated tools: Includes an integrated terminal, debugger, problem list and console output inspector. The project navigator sidebar is simple and powerful: you can manage your files and folders with ease. The command palette helps you find commands by text. The search widget has a powerful auto-complete feature to search and find your files.

        • Extensible and configurable: There are many extensions available for every language supported, including syntax highlighters, IntelliSense and code completion, and debuggers. There are also extension to manage application configuration and architecture like Docker and Jenkins.

        • Integrated with Git: You can visually manage your project repositories, pull, commit and push your changes, and easy conflict resolution.( there is support for SVN (Subversion) users by plugin)

        See more
        Cassandra logo

        Cassandra

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        A partitioned row store. Rows are organized into tables with a required primary key.
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        PROS OF CASSANDRA
        • 115
          Distributed
        • 95
          High performance
        • 80
          High availability
        • 74
          Easy scalability
        • 52
          Replication
        • 26
          Multi datacenter deployments
        • 26
          Reliable
        • 9
          OLTP
        • 7
          Open source
        • 7
          Schema optional
        • 2
          Workload separation (via MDC)
        • 1
          Fast
        CONS OF CASSANDRA
        • 3
          Reliability of replication
        • 1
          Updates

        related Cassandra posts

        Thierry Schellenbach
        Shared insights
        on
        RedisRedisCassandraCassandraRocksDBRocksDB
        at

        1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

        Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

        RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

        This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

        #InMemoryDatabases #DataStores #Databases

        See more
        Umair Iftikhar
        Technical Architect at ERP Studio · | 3 upvotes · 211.2K views

        Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

        My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

        See more
        HBase logo

        HBase

        377
        440
        15
        The Hadoop database, a distributed, scalable, big data store
        377
        440
        + 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

          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

          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
          MySQL logo

          MySQL

          95.4K
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          The world's most popular open source database
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          PROS OF MYSQL
          • 795
            Sql
          • 673
            Free
          • 556
            Easy
          • 527
            Widely used
          • 485
            Open source
          • 180
            High availability
          • 160
            Cross-platform support
          • 104
            Great community
          • 78
            Secure
          • 75
            Full-text indexing and searching
          • 25
            Fast, open, available
          • 14
            SSL support
          • 13
            Robust
          • 13
            Reliable
          • 8
            Enterprise Version
          • 7
            Easy to set up on all platforms
          • 2
            NoSQL access to JSON data type
          • 1
            Replica Support
          • 1
            Relational database
          • 1
            Easy, light, scalable
          • 1
            Sequel Pro (best SQL GUI)
          CONS OF MYSQL
          • 14
            Owned by a company with their own agenda
          • 1
            Can't roll back schema changes

          related MySQL posts

          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
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 1.2M views

          Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

          The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

          https://eng.uber.com/mysql-migration/

          See more
          PostgreSQL logo

          PostgreSQL

          74.4K
          60.1K
          3.5K
          A powerful, open source object-relational database system
          74.4K
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          PROS OF POSTGRESQL
          • 754
            Relational database
          • 508
            High availability
          • 436
            Enterprise class database
          • 380
            Sql
          • 303
            Sql + nosql
          • 171
            Great community
          • 145
            Easy to setup
          • 130
            Heroku
          • 128
            Secure by default
          • 112
            Postgis
          • 48
            Supports Key-Value
          • 46
            Great JSON support
          • 32
            Cross platform
          • 30
            Extensible
          • 26
            Replication
          • 24
            Triggers
          • 22
            Rollback
          • 21
            Multiversion concurrency control
          • 20
            Open source
          • 17
            Heroku Add-on
          • 14
            Stable, Simple and Good Performance
          • 13
            Powerful
          • 12
            Lets be serious, what other SQL DB would you go for?
          • 9
            Good documentation
          • 7
            Scalable
          • 7
            Intelligent optimizer
          • 6
            Reliable
          • 6
            Transactional DDL
          • 6
            Modern
          • 5
            Free
          • 5
            One stop solution for all things sql no matter the os
          • 4
            Relational database with MVCC
          • 3
            Faster Development
          • 3
            Full-Text Search
          • 3
            Developer friendly
          • 2
            Excellent source code
          • 2
            search
          • 2
            Great DB for Transactional system or Application
          • 1
            Full-text
          • 1
            Free version
          • 1
            Open-source
          • 1
            Text
          CONS OF POSTGRESQL
          • 9
            Table/index bloatings

          related PostgreSQL posts

          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
          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
          MongoDB logo

          MongoDB

          72.3K
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          The database for giant ideas
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          PROS OF MONGODB
          • 828
            Document-oriented storage
          • 593
            No sql
          • 549
            Ease of use
          • 465
            Fast
          • 408
            High performance
          • 256
            Free
          • 215
            Open source
          • 180
            Flexible
          • 143
            Replication & high availability
          • 110
            Easy to maintain
          • 42
            Querying
          • 38
            Easy scalability
          • 37
            Auto-sharding
          • 36
            High availability
          • 31
            Map/reduce
          • 27
            Document database
          • 25
            Full index support
          • 25
            Easy setup
          • 16
            Reliable
          • 15
            Fast in-place updates
          • 14
            Agile programming, flexible, fast
          • 12
            No database migrations
          • 8
            Easy integration with Node.Js
          • 8
            Enterprise
          • 6
            Enterprise Support
          • 5
            Great NoSQL DB
          • 3
            Drivers support is good
          • 3
            Aggregation Framework
          • 3
            Support for many languages through different drivers
          • 2
            Awesome
          • 2
            Schemaless
          • 2
            Managed service
          • 2
            Fast
          • 2
            Easy to Scale
          • 1
            Consistent
          • 1
            Acid Compliant
          CONS OF MONGODB
          • 6
            Very slowly for connected models that require joins
          • 3
            Not acid compliant
          • 1
            Proprietary query language

          related MongoDB posts

          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
          Robert Zuber

          We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

          As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

          When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

          See more