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PipelineDB
PipelineDB

6
13
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0
Vitess
Vitess

13
30
+ 1
0
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Vitess vs PipelineDB: What are the differences?

Developers describe Vitess as "A database clustering system for horizontal scaling of MySQL". It is a database solution for deploying, scaling and managing large clusters of MySQL instances. It’s architected to run as effectively in a public or private cloud architecture as it does on dedicated hardware. It combines and extends many important MySQL features with the scalability of a NoSQL database. On the other hand, PipelineDB is detailed as "The Streaming SQL Database". PipelineDB is an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.

Vitess and PipelineDB can be primarily classified as "Databases" tools.

Some of the features offered by Vitess are:

  • Scalability
  • Connection pooling
  • Manageability

On the other hand, PipelineDB provides the following key features:

  • No Application Code
  • Runs on PostgreSQL
  • Eliminate ETL
- No public GitHub repository available -
- No public GitHub repository available -

What is PipelineDB?

PipelineDB is an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.

What is Vitess?

It is a database solution for deploying, scaling and managing large clusters of MySQL instances. It’s architected to run as effectively in a public or private cloud architecture as it does on dedicated hardware. It combines and extends many important MySQL features with the scalability of a NoSQL database.
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          What tools integrate with PipelineDB?
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          What are some alternatives to PipelineDB and Vitess?
          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
          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 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 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 is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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          Decisions about PipelineDB and Vitess
          StackShare Editors
          StackShare Editors
          MySQL
          MySQL
          Vitess
          Vitess

          They're critical to the business data and operated by an ecosystem of tools. But once the tools have been used, it was important to verify that the data remains as expected at all times. Even with the best efforts to prevent errors, inconsistencies are bound to creep at any stage. In order to test the code in a comprehensive manner, Slack developed a structure known as a consistency check framework.

          This is a responsive and personalized framework that can meaningfully analyze and report on your data with a number of proactive and reactive benefits. This framework is important because it can help with repair and recovery from an outage or bug, it can help ensure effective data migration through scripts that test the code post-migration, and find bugs throughout the database. This framework helped prevent duplication and identifies the canonical code in each case, running as reusable code.

          The framework was created by creating generic versions of the scanning and reporting code and an interface for the checking code. The checks could be run from the command line and either a single team could be scanned or the whole system. The process was improved over time to further customize the checks and make them more specific. In order to make this framework accessible to everyone, a GUI was added and connected to the internal administrative system. The framework was also modified to include code that can fix certain problems, while others are left for manual intervention. For Slack, such a tool proved extremely beneficial in ensuring data integrity both internally and externally.

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