delta
Apache Cassandra
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delta | Apache Cassandra | |
---|---|---|
69 | 35 | |
6,847 | 8,507 | |
1.8% | 0.8% | |
9.8 | 9.9 | |
7 days ago | about 15 hours ago | |
Scala | Java | |
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.
delta
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Delta Lake vs. Parquet: A Comparison
Delta is pretty great, let's you do upserts into tables in DataBricks much easier than without it.
I think the website is here: https://delta.io
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Understanding Parquet, Iceberg and Data Lakehouses
I often hear references to Apache Iceberg and Delta Lake as if they’re two peas in the Open Table Formats pod. Yet…
Here’s the Apache Iceberg table format specification:
https://iceberg.apache.org/spec/
As they like to say in patent law, anyone “skilled in the art” of database systems could use this to build and query Iceberg tables without too much difficulty.
This is nominally the Delta Lake equivalent:
https://github.com/delta-io/delta/blob/master/PROTOCOL.md
I defy anyone to even scope out what level of effort would be required to fully implement the current spec, let alone what would be involved in keeping up to date as this beast evolves.
Frankly, the Delta Lake spec reads like a reverse engineering of whatever implementation tradeoffs Databricks is making as they race to build out a lakehouse for every Fortune 1000 company burned by Hadoop (which is to say, most of them).
My point is that I’ve yet to be convinced that buying into Delta Lake is actually buying into an open ecosystem. Would appreciate any reassurance on this front!
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Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake.
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[D] Is there other better data format for LLM to generate structured data?
The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json.
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Delta vs Iceberg: make love not war
Delta 3.0 extends an olive branch. https://github.com/delta-io/delta/releases/tag/v3.0.0rc1
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Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML
Databricks provides Jupyter lab like notebooks for analysis and ETL pipelines using spark through pyspark, sparkql or scala. I think R is supported as well but it doesn't interop as well with their newer features as well as python and SQL do. It interfaces with cloud storage backend like S3 and offers some improvements to the parquet format of data querying that allows for updating, ordering and merged through https://delta.io . They integrate pretty seamlessly to other data visualisation tooling if you want to use it for that but their built in graphs are fine for most cases. They also have ML on rails type through menus and models if I recall but I typically don't use it for that. I've typically used it for ETL or ELT type workflows for data that's too big or isn't stored in a database.
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The "Big Three's" Data Storage Offerings
Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond).
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Ideas/Suggestions around setting up a data pipeline from scratch
As the data source, what I have is a gRPC stream. I get data in protobuf encoded format from it. This is a fixed part in the overall system, there is no other way to extract the data. We plan to ingest this data in delta lake, but before we do that there are a few problems.
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Medallion/lakehouse architecture data modelling
Take a look at Delta Lake https://delta.io, it enables a lot of database-like actions on files
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CSV or Parquet File Format
I prefer parquet (or delta for larger datasets. CSV for very small datasets, or the ones that will be later used/edited in Excel or Googke sheets.
Apache Cassandra
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How to Choose the Right MQTT Data Storage for Your Next Project
Apache Cassandra{:target="_blank"} is a highly scalable and fault-tolerant database that can handle large volumes of data across multiple nodes or clusters. It provides fast read and write operations, making it suitable for real-time analytics or applications with high throughput requirements.
- 10+ Open-Source Projects For Web Developers In 2023
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Database 101: Data Consistency for Beginners
Wide Column: Apache Cassandra, ScyllaDB and DynamoDB
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In One Minute : Hadoop
Cassandra, a replicated, fault-tolerant, decentralized and scalable database system.
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Build Your First App with JavaScript, Node.js, and DataStax Astra DB
A popular database you might already be familiar with is Apache Cassandra®, which powers high-performing applications for thousands of companies including Hulu, Netflix, Spotify, and Apple. While this free, open-source database is known for its high availability, scalability, and resilience; the downside is that it’s also notoriously complex to set up and manage.
- Reducing logging cost by two orders of magnitude using CLP
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Baeldung Series Part 2: Build a Dashboard With Cassandra, Astra and CQL – Mapping Event Data
In our previous article, we looked at augmenting our dashboard to store and display individual events from the Avengers using DataStax Astra, a serverless DBaaS powered by Apache Cassandra using Stargate to offer additional APIs for working with it.
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System Design: CAP theorem
Example: Apache Cassandra, CouchDB.
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Deploy a TikTok Clone with Node.js, Netlify, and DataStax Astra DB
For our TikTok database, we’re using DataStax Astra DB: a cloud-based database that fully manages Apache Cassandra®, one of the most robust and scalable NoSQL databases around.
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System Design: The complete course
Data partitioning in Apache Cassandra.
What are some alternatives?
dvc - 🦉 ML Experiments and Data Management with Git
Druid - Apache Druid: a high performance real-time analytics database.
lakeFS - lakeFS - Data version control for your data lake | Git for data
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
hudi - Upserts, Deletes And Incremental Processing on Big Data.
Scylla - NoSQL data store using the seastar framework, compatible with Apache Cassandra
delta-rs - A native Rust library for Delta Lake, with bindings into Python
Apache HBase - Apache HBase
iceberg - Apache Iceberg
Event Store - EventStoreDB, the event-native database. Designed for Event Sourcing, Event-Driven, and Microservices architectures
Apache Avro - Apache Avro is a data serialization system.
CouchDB - Seamless multi-master syncing database with an intuitive HTTP/JSON API, designed for reliability