Software Alternatives & Reviews

Impala VS Delta Lake

Compare Impala VS Delta Lake and see what are their differences

Impala logo Impala

Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.

Delta Lake logo Delta Lake

Application and Data, Data Stores, and Big Data Tools
  • Impala Landing page
    Landing page //
    2023-04-02
  • Delta Lake Landing page
    Landing page //
    2023-08-26

Impala videos

2016 Chevrolet Impala - Review and Road Test

More videos:

  • Review - 2020 Chevrolet Impala Review | The Final Year
  • Review - Is it the END of the road for the 2019 Chevy Impala?

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

Category Popularity

0-100% (relative to Impala and Delta Lake)
Big Data
100 100%
0% 0
Development
0 0%
100% 100
Data Dashboard
30 30%
70% 70
Office & Productivity
0 0%
100% 100

User comments

Share your experience with using Impala and Delta Lake. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Delta Lake seems to be more popular. It has been mentiond 31 times since March 2021. 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.

Impala mentions (0)

We have not tracked any mentions of Impala yet. Tracking of Impala recommendations started around Mar 2021.

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
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What are some alternatives?

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

Apache Kudu - Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.

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

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

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.

BlueData - BlueData's software platform makes it easier, faster and more cost-effective for organizations to deploy Big Data infrastructure on-premises.

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