A Renewed Mark of Authority: Refreshed DASCA Certification Logos

December 15, 2020

Adding new skills to your career arsenal can be the shortest route to professional growth. But here’s the catch - many professionals pick random skills for upgrades. It is a lost deal and ends up in wasting time, money, effort, and motivation.

So how do you pick a skill that can actually add more value than what you invest? Doing your background research can help. Look for skills gaining traction in the industry and the ones that can add lasting value to your profile.

The popularity of Hadoop Analytics tools was staggering in 2020. We see this trend extending to 2021. Here’s a list of tools that triumphed the charts. Adding these to your CV can up it to the top of the job applications pile. Have a look:

Essential Hadoop Tools in 2021

Traditional databases fail in processing large amount of data. Here’s where big data tools come into the picture. They help analysts and engineers manage huge data sizes.

An open-source framework from Apache, Hadoop stores and processes big data in a distributed environment across computer clusters with simple programming models

Apache Hive

Built on Apache Hadoop, the tool helps in reading, writing, and managing large datasets in distributed storage and queries using SQL syntax. Hive can maximize scalability, meaning more machines can be added to the cluster. It exceeds in its performance, fault tolerance, extensibility, and loose coupling.

Features:

Easy access to data via SQL, helping ETL tasks, reporting, and data analysis
Access to files stored in other data storage systems
Query execution via Apache Tez, Apache Spark, or MapReduce
Structuring a variety of data formats
Procedural language with HPL/SQL
Sub-second query retrieval with Hive LLAP, Apache Hadoop YARN, and Apache Slider

Limitations: Not suitable for online transaction processing (OLTP) workloads

Best use: Traditional data warehousing tasks

Who uses Apache Hive: Facebook, GEICO, Capital One, and Pinterest

Apache Mahout

Apache Mahout creates scalable machine learning algorithms. Big data analytics professionals are using it to implement popular machine learning techniques like recommendation, clustering, and classification.

Features:

Works well in distributed environment
Has a ready-to-use framework for high volume data mining tasks
Provides effective and quick data analysis
Includes MapReduce-enabled clustering implementations
Supports distributed and complementary Naive Bayes classifications
Has distributed fitness function capabilities
Includes matrix and vector libraries

Limitations: Poor visualization and less support for scientific libraries

Who uses Apache Mahout: Adobe, Foursquare, LinkedIn, Facebook, Twitter, and Yahoo!

Apache Impala

Impala improves SQL query performance on Apache Hadoop. It uses the same metadata, ODBC driver, SQL syntax (Hive SQL), and user interface (Hue Beeswax) as Apache Hive. Big data analysts who use Hive can use Impala with little setup.

Features:

No network bottlenecks
A single, open, and unified metadata store
Immediately query-able data
No overhead in data format conversion
A single machine pool needed to scale
All hardware utilized

Limitations: Does not support serialization and deserialization, and can only read a text file

Best use: BI-style Queries on Hadoop, quick implementation, low-latency results, and real-time analysis

Who uses Apache Impala: Stripe, Agoda, Expedia.com, and Looker

Apache Spark

Big data analytics professionals are using Spark for fast and large-scale data processing. Apache Spark provides high-level APIs in Java, Python, Scala, and R. It also has an optimized engine to support general execution graphs. Apache Spark supports Spark SQL, GraphX , MLlib, and structured streaming for incremental computation and stream processing. It needs less maintenance of separate tools.

Features:

Up to 100 times faster in memory, and 10 times faster when running an application on Hadoop cluster on disk
Supports multiple languages and comes with 80 high-level operators for interactive querying
Supports SQL queries, machine learning, streaming data, and graph algorithms

Limitations: No support for real-time processing, no dedicated file management system, expensive

Best use: Streaming data, machine learning, interactive analysis, and fog computing.

Who uses Apache Spark: Alibaba, Apple, Yahoo!, Google, Facebook, and Netflix

Apache Pig

Apache Pig helps big data analysts analyze large data sets with high-level language.

Features:

Easy to write, understand, and maintain complex tasks
Execution automatically optimized by system so the user can focus on semantics
Allows users to create their own functions for special-purpose processing

Limitations: Not the best choice for real-time scenarios, not suitable for pinpointing a record in huge data set

Who uses Apache Pig: Capital One, CVS Health, GEICO, and Microsoft. Yahoo! has been using Apache Pig as the de facto platform for processing big data.

Apache Storm

An open-source and free distributed real time computation system, Apache Storm processes unbounded streams of data. It is simple and can be used with any programming language.

Features:

Many use cases: real-time analytics, continuous computation, online machine learning, distributed RPC, ETL, and more
Fast: over a million tuples processed per second per node
Scalable, fault-tolerant, guarantees your data will be processed
Easy to set up and operate
Integrates with the queueing and database technologies you already use

Limitations: No file management system, no real-time data processing, expensive, and latency issues

Who uses Apache Storm: Groupon, Twitter, The Weather Channel, Yahoo!, WebMD, and Spotify

Apache Sqoop

Apache Sqoop transfers bulk data between Apache Hadoop and structured datastores. It is best used for relational database management systems (RDBMS) like Oracle, MySQL, SQL to the Hadoop Distributed File System (HDFS). Its latest release is 1.4.7.

Features: