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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Apache Impala vs Qubole

Apache Impala vs Qubole

OverviewComparisonAlternatives

Overview

Qubole
Qubole
Stacks36
Followers104
Votes67
Apache Impala
Apache Impala
Stacks145
Followers301
Votes18
GitHub Stars34
Forks33

Apache Impala vs Qubole: What are the differences?

What is Apache Impala? Real-time Query for Hadoop. Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

What is Qubole? Prepare, integrate and explore Big Data in the cloud (Hive, MapReduce, Pig, Presto, Spark and Sqoop). Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Apache Impala belongs to "Big Data Tools" category of the tech stack, while Qubole can be primarily classified under "Big Data as a Service".

Some of the features offered by Apache Impala are:

  • Do BI-style Queries on Hadoop
  • Unify Your Infrastructure
  • Implement Quickly

On the other hand, Qubole provides the following key features:

  • Intuitive GUI
  • Optimized Hive
  • Improved S3 Performance

"Super fast" is the top reason why over 9 developers like Apache Impala, while over 12 developers mention "Simple UI and autoscaling clusters" as the leading cause for choosing Qubole.

Apache Impala is an open source tool with 3 GitHub stars and 4 GitHub forks. Here's a link to Apache Impala's open source repository on GitHub.

Stripe, Agoda, and Expedia.com are some of the popular companies that use Apache Impala, whereas Qubole is used by Pinterest, SaleCycle, and KeepTruckin. Apache Impala has a broader approval, being mentioned in 18 company stacks & 87 developers stacks; compared to Qubole, which is listed in 6 company stacks and 25 developer stacks.

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Detailed Comparison

Qubole
Qubole
Apache Impala
Apache Impala

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Intuitive GUI;Optimized Hive;Improved S3 Performance;Auto Scaling;Spot Instance Pricing;Managed Clusters;Cloud Integration;Cluster Lifecycle Management
Do BI-style Queries on Hadoop;Unify Your Infrastructure;Implement Quickly;Count on Enterprise-class Security;Retain Freedom from Lock-in;Expand the Hadoop User-verse
Statistics
GitHub Stars
-
GitHub Stars
34
GitHub Forks
-
GitHub Forks
33
Stacks
36
Stacks
145
Followers
104
Followers
301
Votes
67
Votes
18
Pros & Cons
Pros
  • 13
    Simple UI and autoscaling clusters
  • 10
    Feature to use AWS Spot pricing
  • 7
    Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters
  • 7
    Real-time data insights through Spark Notebook
  • 6
    Hyper elastic and scalable
Pros
  • 11
    Super fast
  • 1
    Scalability
  • 1
    Distributed
  • 1
    Load Balancing
  • 1
    Massively Parallel Processing
Integrations
Google Compute Engine
Google Compute Engine
Microsoft Azure
Microsoft Azure
Hadoop
Hadoop
Mode
Mode
Redash
Redash
Apache Kudu
Apache Kudu

What are some alternatives to Qubole, Apache Impala?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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