debezium
Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ. (by debezium)
Apache Orc
Apache ORC - the smallest, fastest columnar storage for Hadoop workloads (by apache)
Our great sponsors
debezium | Apache Orc | |
---|---|---|
80 | 4 | |
9,857 | 654 | |
2.0% | 0.9% | |
9.9 | 9.4 | |
3 days ago | about 14 hours ago | |
Java | Java | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
debezium
Posts with mentions or reviews of debezium.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-10.
-
Choosing Between a Streaming Database and a Stream Processing Framework in Python
They manage data in the application layer and your original data stays where it is. This way data consistency is no longer an issue as it was with streaming databases. You can use Change Data Capture (CDC) services like Debezium by directly connecting to your primary database, doing computational work, and saving the result back or sending real-time data to output streams.
-
Generating Avro Schemas from Go types
Both of these articles mention a key player, Debezium. In fact, Debezium has had a place in the modern infrastructure. Let's use a diagram to understand why.
-
debezium VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
-
How the heck do I validate records with this kind of data??
This might be overkill, but you could use an extra tool like https://debezium.io to capture logs about all creates, updates, and deletes in your table
- All the ways to capture changes in Postgres
-
Managed Relational Databases with AWS RDS and Aurora
If you're considering a relational database for an event-driven architecture, check out Debezium. It lets you stream changes to relational databases, and subscribe to change events.
-
Real-time Data Processing Pipeline With MongoDB, Kafka, Debezium And RisingWave
Debezium
-
Postgresql to hadoop in real time
https://debezium.io/ comes to mind as an open source product, but there are a gazillion of these tools out there.
-
ClickHouse Advanced Tutorial: Apply CDC from MySQL to ClickHouse
Contrary to what it sounds, it’s quite straightforward. The database changes are captured via Debezium and published as events on Apache Kafka. ClickHouse consumes those changes in partial order by Kafka Engine. Real-time and eventually consistent.
- Debezium: Stream Changes from Your Database
Apache Orc
Posts with mentions or reviews of Apache Orc.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-01.
-
Java Serialization with Protocol Buffers
The information can be stored in a database or as files, serialized in a standard format and with a schema agreed with your Data Engineering team. Depending on your information and requirements, it can be as simple as CSV, XML or JSON, or Big Data formats such as Parquet, Avro, ORC, Arrow, or message serialization formats like Protocol Buffers, FlatBuffers, MessagePack, Thrift, or Cap'n Proto.
- Personal data of 120,000 Russian servicemen fighting in Ukraine made public
-
AWS EMR Cost Optimization Guide
Data formatting is another place to make gains. When dealing with huge amounts of data, finding the data you need can take up a significant amount of your compute time. Apache Parquet and Apache ORC are columnar data formats optimized for analytics that pre-aggregate metadata about columns. If your EMR queries column intensive data like sum, max, or count, you can see significant speed improvements by reformatting data like CSVs into one of these columnar formats.
-
Apache Hudi - The Streaming Data Lake Platform
The following stack captures layers of software components that make up Hudi, with each layer depending on and drawing strength from the layer below. Typically, data lake users write data out once using an open file format like Apache Parquet/ORC stored on top of extremely scalable cloud storage or distributed file systems. Hudi provides a self-managing data plane to ingest, transform and manage this data, in a way that unlocks incremental data processing on them.
What are some alternatives?
When comparing debezium and Apache Orc you can also consider the following projects:
maxwell - Maxwell's daemon, a mysql-to-json kafka producer
Protobuf - Protocol Buffers - Google's data interchange format
kafka-connect-bigquery - A Kafka Connect BigQuery sink connector
Apache Parquet - Apache Parquet
realtime - Broadcast, Presence, and Postgres Changes via WebSockets
Apache Avro - Apache Avro is a data serialization system.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
hudi - Upserts, Deletes And Incremental Processing on Big Data.
Apache Thrift - Apache Thrift
RocksDB - A library that provides an embeddable, persistent key-value store for fast storage.
tape - A lightning fast, transactional, file-based FIFO for Android and Java.