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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Alooma vs Impala

Alooma vs Impala

OverviewComparisonAlternatives

Overview

Apache Impala
Apache Impala
Stacks145
Followers301
Votes18
GitHub Stars34
Forks33
Alooma
Alooma
Stacks24
Followers47
Votes0

Alooma vs Impala: What are the differences?

Developers describe Alooma as "Integrate any data source like databases, applications, and any API - with your own Amazon Redshift". Get the power of big data in minutes with Alooma and Amazon Redshift. Simply build your pipelines and map your events using Alooma’s friendly mapping interface. Query, analyze, visualize, and predict now. On the other hand, Impala is detailed as "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.

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

Impala is an open source tool with 2.17K GitHub stars and 825 GitHub forks. Here's a link to Impala's open source repository on GitHub.

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Detailed Comparison

Apache Impala
Apache Impala
Alooma
Alooma

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.

Get the power of big data in minutes with Alooma and Amazon Redshift. Simply build your pipelines and map your events using Alooma’s friendly mapping interface. Query, analyze, visualize, and predict now.

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
34
GitHub Stars
-
GitHub Forks
33
GitHub Forks
-
Stacks
145
Stacks
24
Followers
301
Followers
47
Votes
18
Votes
0
Pros & Cons
Pros
  • 11
    Super fast
  • 1
    High Performance
  • 1
    Distributed
  • 1
    Scalability
  • 1
    Replication
No community feedback yet
Integrations
Hadoop
Hadoop
Mode
Mode
Redash
Redash
Apache Kudu
Apache Kudu
Chartio
Chartio
PostgreSQL
PostgreSQL
MongoDB
MongoDB
Salesforce Sales Cloud
Salesforce Sales Cloud
Cassandra
Cassandra
Amazon Redshift
Amazon Redshift
MySQL
MySQL
Marketo
Marketo
Mixpanel
Mixpanel
Google Analytics
Google Analytics

What are some alternatives to Apache Impala, Alooma?

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.

Qubole

Qubole

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

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.

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