Impala Use Cases and Applications: Know Where To Use Impala

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After learning Impala Introduction, we will learn why and where we use it. However, there are many Impala Use Cases. So, in this article, “Impala Applications and Use Cases ”, we will learn several Impala Use Cases and Applications in detail.

However, before them, we will focus on, why actually we use Impala to understand its applications well.

Why is Impala used?

Basically, on top of Hadoop ecosystem, Impala provides parallel processing database technology. Also, we can perform low latency queries interactively by using it.

As we very well know in launching and processing queries Hive MapReduce job will take minimum time. But while it comes to impala it gives results in seconds.

Moreover, to perform analytics on data stored in Hadoop File System Impala being real-time query engine best suited for analytics and for data scientists.

Also, we can say it is the best fit for reporting tools or visualization tools like Pentaho, Tableau since, it gives results in real-time.

In addition, Impala comes with an inbuilt support for processing all Hadoop supported file formats (ORC, Parquet, ..,). Also, for real-time interaction with the data on Hadoop Distributed Filesystem or the tables already exist in Hive Impala performs very well.

Moreover, it does support snappy compression, which is the default compression codec of Hive or Hadoop. Below is the list of Impala Use cases and Applications.

Impala Use Cases and Applications

a. Do BI-style Queries on Hadoop

While it comes to BI/analytic queries on Hadoop especially those which are not delivered by batch frameworks such as Apache Hive, Impala offers low latency and high concurrency for them. Moreover, it scales linearly, even in multi-tenant environments.

b. Unify Your Infrastructure

In Impala, there is no redundant infrastructure or data conversion/duplication is possible. That implies we need to utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment.

c. Implement Quickly

Basically, Impala utilizes the same metadata and ODBC driver for Apache Hive users. Such as Hive, Impala supports SQL. Hence, we do not require to think about re-inventing the implementation wheel.

d. Count on Enterprise-class Security

However, there is a beautiful feature of Authentication. So, for that Impala is integrated with native Hadoop security and Kerberos. Moreover, we can also ensure that the right users and applications are authorized for the right data by using the Sentry module.

e. Retain Freedom from Lock-in

It is available easily, which mean it is an Open source (Apache License).

f. Expand the Hadoop User-verse

Also, it offers flexibility that more users, can interact with more data through a single repository and metadata store from the source through analysis. Whether those users are using SQL queries or BI applications.

g. Low Latent Results

While we don’t need low latent results, we can use Impala.

h. Partial Data Analyzation

Moreover, when we require analyzing Partial data only, we use Impala.

i. Quick Analysis

Also, when we need to perform quick analysis, we use Impala.

j. Real-time

Moreover, it obtains results in real time.

Impala from the User’s Perspective

  • Basically, to play well with BI tools it is designed.
  • Moreover, there are Standard ANSI SQL (92, with 2003 analytic extensions), UDFs/UDAs, correlated subqueries, nested types, …
  • Supports Data types: Integer and floating point tpes, STRING, CHAR, VARCHAR, TIMESTAMP
  • However, we get  dECIMAL(<precision>, <scale>) with up to 38 digits of precision.
  • Possible to connect via odbc/JDBC.
  • It authenticates via Kerberos/LDAP.
  • Also, authorization offers with GRANT/REVOKE.


Hence, we have seen several Impala Applications and Use Cases in detail. However, if any doubts occur regarding, feel free to ask in the comment section.

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