delta
Apache Kafka
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delta | Apache Kafka | |
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69 | 25 | |
6,782 | 27,123 | |
1.9% | 1.4% | |
9.8 | 9.9 | |
6 days ago | about 4 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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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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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.
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How to build a data pipeline using Delta Lake
This sounds like a new trending destination to take selfies in front of, but it’s even better than that. Delta Lake is an “open-source storage layer designed to run on top of an existing data lake and improve its reliability, security, and performance.” (source). It let’s you interact with an object storage system like you would with a database.
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Delta.io/deltalake self hosting
I mean the different between using the delta.io framework to let it run on your own machines/ vms vs using databricks and have clusters defined.
You are right, delta.io is just a framework. Sorry for the unclear question. Another try: when you host spark on your own with delta as table format compared to usage of Databricks, what are the differences?
Apache Kafka
- Scala DevInTraining looking to contribute to projects
- *bip*
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What is Kafka ?
Source and documentation on GitHub
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Can someone please eli5 how the hierarchical timing wheel algorithm works?
I briefly described the algorithm in this article and there is a wonderful article from Kafka that goes into more depth in their general purpose implementation. My implementation is specialized and over optimized in comparison, e.g. by using bit manipulation to avoid more expensive division/modulus instructions. Tokio rewrote their timerwheel after I showed them mine, borrowing some ideas but also staying more general. Hope that helps!
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How-to-Guide: Contributing to Open Source
Apache Kafka
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I am proud to announce, a new Sorting algorithm!
AFAIK, the Linux kernel actually uses a LinkedList for this (Ref: workqueue.c, types.h) and message queues use Timing Wheel (Ref: Kafka's TimingWheel)
- Project Ideas Thread
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Which diagram tool Kafka using in its documentation
Looks like the author for that image is Guozhang Wang, who is still active in the kafka repo.
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How to get `byte[]` as `byte[]` in a Kafka Record (in an SMT)
Perhaps you are looking for org.apache.kafka.connect.converters.ByteArrayConverter?
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Open Source Analytics Stack: Bringing Control, Flexibility, and Data-Privacy to Your Analytics
With the increase in real-time data streams and event streams, certain use cases emerged that require access to real-time data such as financial services risk reporting or detecting a credit card fraud. Real-time streams can be obtained using a stream processing framework like Apache Kafka (website, GitHub). The focus is to direct the stream of data from various sources into reliable queues where data can be automatically transformed, stored, analyzed and reported concurrently.
What are some alternatives?
celery - Distributed Task Queue (development branch)
Apache ActiveMQ Artemis - Mirror of Apache ActiveMQ Artemis
redpanda - Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
jetstream - JetStream Utilities
Aeron - Efficient reliable UDP unicast, UDP multicast, and IPC message transport
NATS - High-Performance server for NATS.io, the cloud and edge native messaging system.
Apache Qpid - Mirror of Apache Qpid
Hermes - Fast and reliable message broker built on top of Kafka.
JBoss HornetQ - HornetQ is an open source project to build a multi-protocol, embeddable, very high performance, clustered, asynchronous messaging system.
Chronicle Queue - Micro second messaging that stores everything to disk
Apache RocketMQ - Apache RocketMQ is a cloud native messaging and streaming platform, making it simple to build event-driven applications.
Apache Camel - Apache Camel is an open source integration framework that empowers you to quickly and easily integrate various systems consuming or producing data.