Software Alternatives & Reviews

Delta Lake VS Apache Arrow

Compare Delta Lake VS Apache Arrow and see what are their differences

Delta Lake logo Delta Lake

Application and Data, Data Stores, and Big Data Tools

Apache Arrow logo Apache Arrow

Apache Arrow is a cross-language development platform for in-memory data.
  • Delta Lake Landing page
    Landing page //
    2023-08-26
  • Apache Arrow Landing page
    Landing page //
    2021-10-03

Delta Lake videos

A Thorough Comparison of Delta Lake, Iceberg and Hudi

More videos:

  • Tutorial - Delta Lake for apache Spark | How does it work | How to use delta lake | Delta Lake for Spark ACID
  • Review - ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scale Storage and Analytics

Apache Arrow videos

Wes McKinney - Apache Arrow: Leveling Up the Data Science Stack

More videos:

  • Review - "Apache Arrow and the Future of Data Frames" with Wes McKinney
  • Review - Apache Arrow Flight: Accelerating Columnar Dataset Transport (Wes McKinney, Ursa Labs)

Category Popularity

0-100% (relative to Delta Lake and Apache Arrow)
Development
100 100%
0% 0
Databases
38 38%
62% 62
Office & Productivity
100 100%
0% 0
NoSQL Databases
0 0%
100% 100

User comments

Share your experience with using Delta Lake and Apache Arrow. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Apache Arrow might be a bit more popular than Delta Lake. We know about 33 links to it since March 2021 and only 31 links to Delta Lake. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Delta Lake mentions (31)

  • 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. - Source: Hacker News / 3 months ago
  • 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. - Source: dev.to / 4 months ago
  • [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. Source: 5 months ago
  • 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... - Source: Hacker News / 10 months ago
  • 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). Source: 11 months ago
View more

Apache Arrow mentions (33)

  • How moving from Pandas to Polars made me write better code without writing better code
    In comes Polars: a brand new dataframe library, or how the author Ritchie Vink describes it... a query engine with a dataframe frontend. Polars is built on top of the Arrow memory format and is written in Rust, which is a modern performant and memory-safe systems programming language similar to C/C++. - Source: dev.to / about 2 months ago
  • Time Series Analysis with Polars
    One is related to the heritage of being built around the NumPy library, which is great for processing numerical data, but becomes an issue as soon as the data is anything else. Pandas 2.0 has started to bring in Arrow, but it's not yet the standard (you have to opt-in and according to the developers it's going to stay that way for the foreseeable future). Also, pandas's Arrow-based features are not yet entirely on... - Source: dev.to / 5 months ago
  • TXR Lisp
    IMO a good first step would be to use the txr FFI to write a library for Apache arrow: https://arrow.apache.org/. - Source: Hacker News / 5 months ago
  • A Polars exploration into Kedro
    Polars is an open-source library for Python, Rust, and NodeJS that provides in-memory dataframes, out-of-core processing capabilities, and more. It is based on the Rust implementation of the Apache Arrow columnar data format (you can read more about Arrow on my earlier blog post “Demystifying Apache Arrow”), and it is optimised to be blazing fast. - Source: dev.to / 11 months ago
  • Demystifying Apache Arrow
    Apache Arrow (Arrow for short) is an open source project that defines itself as "a language-independent columnar memory format" (more on that later). It is part of the Apache Software Foundation, and as such is governed by a community of several stakeholders. It has implementations in several languages (C++ and also Rust, Julia, Go, and even JavaScript) and bindings for Python, R and others that wrap the C++... - Source: dev.to / 12 months ago
View more

What are some alternatives?

When comparing Delta Lake and Apache Arrow, you can also consider the following products

Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

GeoSpock - GeoSpock is the platform for data lake management, providing a unified view of the data assets within an organization and making it easily accessible.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Cloud Dataprep - Cloud Dataprep by Trifacta is a data prep & cleansing service for exploring, cleaning & preparing datasets using a simple drag & drop browser environment

Apache Parquet - Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.