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Azure Databricks VS Impala

Compare Azure Databricks VS Impala and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Azure Databricks logo Azure Databricks

Azure Databricks is a fast, easy, and collaborative Apache Spark-based big data analytics service designed for data science and data engineering.

Impala logo Impala

Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.
  • Azure Databricks Landing page
    Landing page //
    2023-04-02
  • Impala Landing page
    Landing page //
    2023-04-02

Azure Databricks videos

+ Add

Azure Databricks is Easier Than You Think

More videos:

  • Review - Ingest, prepare & transform using Azure Databricks & Data Factory | Azure Friday
  • Review - Azure Databricks - What's new! | DB102

Impala videos

+ Add

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?

Category Popularity

0-100% (relative to Azure Databricks and Impala)

Azure Databricks

Impala

Technical Computing
100 100%
Technical Computing
0% 0
Big Data
0 0%
Big Data
100% 100
Business & Commerce
100 100%
Business & Commerce
0% 0
Data Dashboard
56 56%
Data Dashboard
44% 44

User comments

Share your experience with using Azure Databricks and Impala. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Azure Databricks and Impala

Azure Databricks Reviews

10 Best Big Data Analytics Tools For Reporting In 2022
Azure Databricks is a data analytics tool optimized for Microsoft’s Azure cloud services solution. It provides three development environments for data-intensive apps, namely Databricks SQL, Databricks Machine Learning, and Databricks Data Science & Engineering.The platform supports languages including Python, Java, R, Scala, and SQL, plus data science frameworks and...
Source: theqalead.com

Impala Reviews

We have no reviews of Impala yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Azure Databricks seems to be more popular. It has been mentiond 2 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.

Azure Databricks mentions (2)

  • Top 30 Microsoft Azure Services
    In the big data space, Azure offers Azure Databricks. This is an Apache Spark big data analytics and machine learning service over a Distributed File System. The distributed cluster of nodes running analytics and AI operations in parallel allow for fast processing of large volumes of data and integration with popular machine learning libraries such as PyTorch unleash endless possibilities for custom ML. - Source: dev.to / almost 3 years ago
  • ZooKeeper-free Kafka is out. First Demo
    https://azure.microsoft.com/en-us/services/databricks. - Source: Hacker News / about 3 years ago

Impala mentions (0)

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

What are some alternatives?

When comparing Azure Databricks and Impala, you can also consider the following products

IBM Cloud Pak for Data - Move to cloud faster with IBM Cloud Paks running on Red Hat OpenShift – fully integrated, open, containerized and secure solutions certified by IBM.

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

MicroStrategy - MicroStrategy is a cloud-based platform providing business intelligence, mobile intelligence and network applications.

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

Arcadia Enterprise - Arcadia Enterprise is the ultimate native BI for data lakes with real-time streaming visualizations, all without adding hardware or moving data.

SQream - SQream empowers organizations to analyze the full scope of their Massive Data, from terabytes to petabytes, to achieve critical insights which were previously unattainable.

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