Greenplum
Airflow
Our great sponsors
Greenplum | Airflow | |
---|---|---|
9 | 169 | |
6,198 | 34,397 | |
1.0% | 2.1% | |
9.9 | 10.0 | |
5 days ago | 7 days ago | |
C | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
Greenplum
-
Ask HN: It's 2023, how do you choose between MySQL and Postgres?
Friends don't let their friends choose Mysql :)
A super long time ago (decades) when I was using Oracle regularly I had to make a decision on which way to go. Although Mysql then had the mindshare I thought that Postgres was more similar to Oracle, more standards compliant, and more of a real enterprise type of DB. The rumor was also that Postgres was heavier than MySQL. Too many horror stories of lost data (MyIsam), bad transactions (MyIsam lacks transaction integrity), and the number of Mysql gotchas being a really long list influenced me.
In time I actually found out that I had underestimated one of the most important attributes of Postgres that was a huge strength over Mysql: the power of community. Because Postgres has a really superb community that can be found on Libera Chat and elsewhere, and they are very willing to help out, I think Postgres has a huge advantage over Mysql. RhodiumToad [Andrew Gierth] https://github.com/RhodiumToad & davidfetter [David Fetter] https://www.linkedin.com/in/davidfetter are incredibly helpful folks.
I don't know that Postgres' licensing made a huge difference or not but my perception is that there are a ton of 3rd party products based on Postgres but customized to specific DB needs because of the more liberalness of the PG license which is MIT/BSD derived https://www.postgresql.org/about/licence/
Some of the PG based 3rd party DBs:
Enterprise DB https://www.enterprisedb.com/ - general purpose PG with some variants
Greenplum https://greenplum.org/ - Data warehousing
Crunchydata https://www.crunchydata.com/products/hardened-postgres - high security Postgres for regulated environments
Citus https://www.citusdata.com - Distributed DB & Columnar
Timescale https://www.timescale.com/
Why Choose PG today?
If you want better ACID: Postgres
If you want more compliant SQL: Postgres
If you want more customizability to a variety of use-cases: Postgres using a variant
If you want the flexibility of using NOSQL at times: Postgres
If you want more product knowledge reusability for other backend products: Postgres
-
Show HN: Postgres WASM
I was wondering if anyone had thought about using this to experiment with the planner.
The engineering and support teams at Greenplum, a fork of Postgres, have a tool (minirepro[0]) which, given a sql query, can grab a minimal set of DDLs and the associated statistics for the tables involved in the query that can then be loaded into a "local" GPDB instance. Having the DDL and the statistics meant the team was able to debug issues in the optimizer (example [1]), without having access to a full set of data. This approach, if my understanding is correct, could be enabled in the browser with this Postgres WASM capability.
[0] https://github.com/greenplum-db/gpdb/blob/6X_STABLE/gpMgmt/b...
-
Amazon Aurora's Read/Write Capability Enhancement with Apache ShardingSphere-Proxy
A database solution architect at AWS, with over 10 years of experience in the database industry. Lili has been involved in the R&D of the Hadoop/Hive NoSQL database, enterprise-level database DB2, distributed data warehouse Greenplum/Apache HAWQ and Amazon’s cloud native database.
- Greenplum Database – Massively Parallel PostgreSQL for Analytics
-
What’s the Database Plus concept and what challenges can it solve?
Today, it is normal for enterprises to leverage diversified databases. In my market of expertise, China, in the Internet industry, MySQL together with data sharding middleware is the go to architecture, with GreenPlum, HBase, Elasticsearch, Clickhouse and other big data ecosystems being auxiliary computing engine for analytical data. At the same time, some legacy systems (such as SQLServer legacy from .NET transformation, or Oracle legacy from outsourcing) can still be found in use. In the financial industry, Oracle or DB2 is still heavily used as the core transaction system. New business is migrating to MySQL or PostgreSQL. In addition to transactional databases, analytical databases are increasingly diversified as well.
-
Data Science Competition
Green Plum
-
Inspecting joins in PostgreSQL
PostgreSQL is a free and advanced database system with the capacity to handle a lot of data. It’s available for very large data in several forms like Greenplum and Redshift on Amazon. It is open source and is managed by an organized and very principled community.
-
What’s so special about distributed SQL? Ask us anything!
2003 - https://greenplum.org/
-
Using Postgres as a Data Warehouse
There's Greenplum!
Airflow
-
Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions
Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.
-
Navigating Week Two: Insights and Experiences from My Tublian Internship Journey
In week Two, I contributed to the Apache Airflow repository.
-
Airflow VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
-
Best ETL Tools And Why To Choose
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
-
Simplifying Data Transformation in Redshift: An Approach with DBT and Airflow
Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.
-
Share Your favorite python related software!
AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!
-
Ask HN: What is the correct way to deal with pipelines?
I agree there are many options in this space. Two others to consider:
- https://airflow.apache.org/
- https://github.com/spotify/luigi
There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…
- "Você veio protestar para ter acesso ao código fonte da urnas. O que é o código fonte?" "Não sei" 🤡
- Cómo construir tu propia data platform. From zero to hero.
-
Is it impossible to contribute to open source as a data engineer?
You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte
What are some alternatives?
citus - Distributed PostgreSQL as an extension
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
dagster - An orchestration platform for the development, production, and observation of data assets.
ClickHouse - ClickHouse® is a free analytics DBMS for big data
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
cockroach - CockroachDB - the open source, cloud-native distributed SQL database.
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
dremio-oss - Dremio - the missing link in modern data
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL. [Moved to: https://github.com/apache/age]
Dask - Parallel computing with task scheduling