Top 5 Hadoop Analytics Tools – Take a Dive into Advanced Analytics

Hadoop is an open source distributed storage and processing framework. It is at the center of the growing big data ecosystem. It gets used for advanced analytics which includes predictive analytics, data mining and machine learning. Hadoop is a technology which manages data processing and storage for big data applications. And can work with various forms of structured and unstructured data. So, let’s explore Hadoop Analytics Tools.

Hadoop Analytics Tools

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List of Top Hadoop Analytics Tools

Below are the top 5 Hadoop Analytics Tools, let’s discuss them in detail –

1. Spark

Apache Spark provides in-memory data processing for developers and data scientists. Its easy development, flexibility, and speed have made it one of the popular Apache projects. It is the successor to MapReduce as a standard execution engine for Hadoop. Apache spark enables real-time, batch and advanced analytics over Hadoop platform. Spark is increasingly becoming the default data execution engine for analytics workload.

Features of Spark:

  • Ability to cache datasets to perform interactive data analysis. Extract a working set, cache it and query it repeatedly.
  • Interactive command line interface in Scala or in Python for low latency data exploration
  • High-level library for stream processing, through Spark Streaming.
  • High-level libraries for machine learning and graph processing. Spark is ten times much faster than disk-based apache mahout because of its distributed memory-based architecture.

2. Apache Impala

Apache Impala provides massively parallel processing SQL analytics. It opens up interactive BI for the business analyst. Apache Impala is great at performance and concurrency requirements. These are features which are necessary for building an analytic database. It is natively integrated with Hadoop and leading BI tools to provide with a low-cost platform for analytics.

Features of Impala:

  • Performance equivalent to leading MPP (Massively Parallel Processing) databases.
  • Faster time to insight than traditional databases. Faster interactive analytics directly on data stored in Hadoop.
  • Cost savings due to reduced data movement, modeling, and storage.
  • A more complete analysis of historical and raw data. Without information loss due to aggregation or conforming to the fixed schema.
  • Freedom from vendor lock-in through open source Apache license.
  • Security with Kerberos authentication. And role-based authorization through apache sentry project.

3. MapReduce

Hadoop MapReduce is a framework for writing applications to process a huge amount of data. They do so in parallel on a large cluster of commodity hardware in a reliable and fault tolerant manner. The job submitted by the client gets divided into a number of independent tasks. These tasks run in parallel giving high throughput. The Map-reduce job is majorly divided into Map tasks and reduce tasks. Usually, programmers write the entire business logic in the map task. And reduce task perform summarization on the input dataset.

Features of Hadoop MapReduce:

  • Easily scalable architecture. Can add machines to increase the processing power of the cluster.
  • Fault Tolerance – It automatically and seamlessly recovers from failure.
  • Load Balancing – Intra datanode balancer which we can invoke through CLI. Resolves the data skew issue within a node.
  • Security – POSIX based file permissions for users and groups with optional LDAP integration.

4. Mahout

Apache Mahout is a library of various scalable machine learning algorithms. It gets implemented on the top of Hadoop using Map-Reduce paradigm. Machine learning is the discipline of Artificial Intelligence. It is focused on enabling machines to learn without being explicitly programmed. It is commonly used to improve performance in the future based on previous outcomes.

Features of Mahout:

  • Collaborative filtering is mining user behavior and making product recommendations.
  • Clustering is taking items from a particular class and organizing them in naturally occurring groups. In such a way that items occurring in the same group are similar to each other.
  • Classification is learning from existing categorization and then assigning unclassified items to the best categories.

5. Apache Hive

Apache Hive is a data warehouse software. It facilitates querying and managing large datasets residing in distributed storage. Hive provides a mechanism to query data using SQL like language i.e. HQL. At the same time, this language allows map-reduce programmers to plug in their source code i.e. mappers and reducers. This is when it is inconvenient or inefficient to express the logic in HiveQL.


  • Provides indexing to accelerate the process. Indexing type includes bitmap and compaction indexes.
  • Has different storage types such as HBase, RC files, ORC, plain text and others.
  • Metadata storage in RDBMS significantly reduces the time taken for semantic checks.
  • Operating on compressed data stored in the Hadoop ecosystem is possible through algorithms like gzip, Bzip2, snappy, etc.
  • We can have user-defined functions to manipulate data and strings. Hive supports extending UDF to handle use cases not supported by built-in functions.
  • SQL like queries that is HiveQL which are implicitly converted into MapReduce jobs.

So, this was all about Hadoop Analytics Tools. Hope you liked our explanation.


Hadoop is great for MapReduce data analysis on the huge amount of data. Its specific use cases include data searching, data reporting, large scale indexing of files i.e. log files or data from web crawlers. Mahout lets you analyze large sets of data effectively in less time. Impala makes processing data easy. It gives response quickly in real-time. The performance of the repeated query is even better. Apache Spark gives high performance for both batch and streaming data. It uses state-of-the-art DAG scheduler, query optimizer, and physical execution engine.

Still, if you have any query regarding Hadoop Analytics Tools, ask in the comment tab.

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