Alternatives to PipelineDB logo

Alternatives to PipelineDB

TimescaleDB, Apache Spark, RethinkDB, InfluxDB, and Kafka are the most popular alternatives and competitors to PipelineDB.
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What is PipelineDB and what are its top alternatives?

PipelineDB is an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.
PipelineDB is a tool in the Databases category of a tech stack.

Top Alternatives to PipelineDB

  • TimescaleDB

    TimescaleDB

    TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud. ...

  • Apache Spark

    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. ...

  • RethinkDB

    RethinkDB

    RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn. ...

  • InfluxDB

    InfluxDB

    InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out. ...

  • Kafka

    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • KSQL

    KSQL

    KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time. ...

  • 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. ...

PipelineDB alternatives & related posts

TimescaleDB logo

TimescaleDB

137
226
41
Scalable and reliable time-series SQL database optimized for fast ingest and complex queries. Built on PostgreSQL.
137
226
+ 1
41
PROS OF TIMESCALEDB
  • 8
    Open source
  • 7
    Easy Query Language
  • 6
    Time-series data analysis
  • 5
    Established postgresql API and support
  • 4
    Reliable
  • 2
    Paid support for automatic Retention Policy
  • 2
    Fast and scalable
  • 2
    Chunk-based compression
  • 2
    Postgres integration
  • 2
    High-performance
  • 1
    Case studies
CONS OF TIMESCALEDB
  • 2
    Licensing issues when running on managed databases

related TimescaleDB posts

John Kodumal

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

See more
Mauro Bennici
CTO at You Are My GUide · | 7 upvotes · 39.2K views

PostgreSQL plus TimescaleDB allow us to concentrate the business effort on how to analyze valuable data instead of manage them on IT side. We are now able to ingest thousand of social shares "managed" data without compromise the scalability of the system or the time query. TimescaleDB is transparent to PostgreSQL , so we continue to use the same SQL syntax without any changes. At the same time, because we need to manage few document objects we dismissed the MongoDB cluster.

See more
Apache Spark logo

Apache Spark

2.2K
2.6K
132
Fast and general engine for large-scale data processing
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2.6K
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132
PROS OF APACHE SPARK
  • 58
    Open-source
  • 48
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query
CONS OF APACHE SPARK
  • 3
    Speed

related Apache Spark posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 1.8M views

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

See more
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 935.6K 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
RethinkDB logo

RethinkDB

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JSON. Scales to multiple machines with very little effort. Open source.
286
373
+ 1
307
PROS OF RETHINKDB
  • 48
    Powerful query language
  • 46
    Excellent dashboard
  • 42
    JSON
  • 41
    Distributed database
  • 38
    Open source
  • 25
    Reactive
  • 16
    Atomic updates
  • 15
    Joins
  • 9
    MVCC concurrency
  • 9
    Hadoop-style map/reduce
  • 4
    Geospatial support
  • 4
    Real-time, open-source, scalable
  • 2
    Great Admin UI
  • 2
    A NoSQL DB with joins
  • 2
    YC Company
  • 2
    Fast, easily scalable, great customer support
  • 2
    Changefeeds: no polling needed to get updates
CONS OF RETHINKDB
    Be the first to leave a con

    related RethinkDB posts

    Łukasz Korecki
    CTO & Co-founder at EnjoyHQ · | 12 upvotes · 119.4K views

    We initially chose RethinkDB because of the schema-less document store features, and better durability resilience/story than MongoDB In the end, it didn't work out quite as we expected: there's plenty of scalability issues, it's near impossible to run analytical workloads and small community makes working with Rethink a challenge. We're in process of migrating all our workloads to PostgreSQL and hopefully, we will be able to decommission our RethinkDB deployment soon.

    See more
    InfluxDB logo

    InfluxDB

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    845
    161
    An open-source distributed time series database with no external dependencies
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    845
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    161
    PROS OF INFLUXDB
    • 49
      Time-series data analysis
    • 28
      Easy setup, no dependencies
    • 24
      Fast, scalable & open source
    • 21
      Open source
    • 18
      Real-time analytics
    • 6
      Continuous Query support
    • 5
      Easy Query Language
    • 4
      HTTP API
    • 4
      Out-of-the-box, automatic Retention Policy
    • 1
      Offers Enterprise version
    • 1
      Free Open Source version
    CONS OF INFLUXDB
    • 4
      Instability
    • 1
      HA or Clustering is only in paid version

    related InfluxDB posts

    Kafka logo

    Kafka

    13.9K
    13K
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    Distributed, fault tolerant, high throughput pub-sub messaging system
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    13K
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    557
    PROS OF KAFKA
    • 119
      High-throughput
    • 113
      Distributed
    • 85
      Scalable
    • 78
      High-Performance
    • 64
      Durable
    • 35
      Publish-Subscribe
    • 17
      Simple-to-use
    • 14
      Open source
    • 10
      Written in Scala and java. Runs on JVM
    • 6
      Message broker + Streaming system
    • 4
      Avro schema integration
    • 2
      Suport Multiple clients
    • 2
      Robust
    • 2
      KSQL
    • 2
      Partioned, replayable log
    • 1
      Fun
    • 1
      Extremely good parallelism constructs
    • 1
      Simple publisher / multi-subscriber model
    • 1
      Flexible
    CONS OF KAFKA
    • 27
      Non-Java clients are second-class citizens
    • 26
      Needs Zookeeper
    • 7
      Operational difficulties
    • 2
      Terrible Packaging

    related Kafka posts

    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 1.8M views

    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

    See more
    John Kodumal

    As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

    We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

    See more
    KSQL logo

    KSQL

    34
    85
    5
    Open source streaming SQL for Apache Kafka
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    85
    + 1
    5
    PROS OF KSQL
    • 3
      Streamprocessing on Kafka
    • 2
      SQL syntax with windowing functions over streams
    • 0
      Easy transistion for SQL Devs
    CONS OF KSQL
      Be the first to leave a con

      related KSQL posts

      MySQL logo

      MySQL

      75.2K
      59.5K
      3.7K
      The world's most popular open source database
      75.2K
      59.5K
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      3.7K
      PROS OF MYSQL
      • 790
        Sql
      • 673
        Free
      • 557
        Easy
      • 527
        Widely used
      • 485
        Open source
      • 180
        High availability
      • 158
        Cross-platform support
      • 103
        Great community
      • 78
        Secure
      • 75
        Full-text indexing and searching
      • 25
        Fast, open, available
      • 14
        SSL support
      • 13
        Reliable
      • 13
        Robust
      • 8
        Enterprise Version
      • 7
        Easy to set up on all platforms
      • 2
        NoSQL access to JSON data type
      • 1
        Replica Support
      • 1
        Easy, light, scalable
      • 1
        Relational database
      • 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 · 979.1K 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

      56.7K
      44.6K
      3.5K
      A powerful, open source object-relational database system
      56.7K
      44.6K
      + 1
      3.5K
      PROS OF POSTGRESQL
      • 753
        Relational database
      • 506
        High availability
      • 436
        Enterprise class database
      • 379
        Sql
      • 298
        Sql + nosql
      • 171
        Great community
      • 145
        Easy to setup
      • 129
        Heroku
      • 128
        Secure by default
      • 111
        Postgis
      • 48
        Supports Key-Value
      • 46
        Great JSON support
      • 32
        Cross platform
      • 29
        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
        Modern
      • 6
        Reliable
      • 6
        Transactional DDL
      • 5
        One stop solution for all things sql no matter the os
      • 5
        Free
      • 4
        Relational database with MVCC
      • 3
        Faster Development
      • 3
        Full-Text Search
      • 3
        Developer friendly
      • 2
        Excellent source code
      • 2
        Great DB for Transactional system or Application
      • 2
        search
      • 1
        Free version
      • 1
        Open-source
      • 1
        Full-text
      • 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