Big Data

Top 15 Big data tools and technologies that will land you a big data job

February 23, 2020

So, you are aiming to land a big data job in 2018. The big data rush is at its peak. No surprise that all the global giants want to harvest and decipher the massive datasets for good. Simultaneously many new data tools and technologies have emerged for Big Data.

But they can’t run them without the brightest talents! At the end of this article, you’ll be able to know about;

  1. Fifteen Big-data tools and technologies being employed globally
  2. The significance of each tool and technology
  3. How that particular tool or technology can help you land a job in 2018?

Possessing a knowledge of these tools & technologies will significantly boost your worth in the ‘big data market.’ Let us get started!

This is the list of fifteen big data tools and technologies that will land you a big data job in 2018. Take a look.

The velocity, variety, and volume of the unstructured data are tedious to manage. Capturing, filtering and storing it for analysis and predictions is a real challenge. New software tools, technologies, and techniques are constantly developed to address this challenge. Hence, these demand for an ‘upgraded’ expertise of the professionals in this field. Listing the skills set of these new ‘tools’ in your resume will let the organizations see the potential value you can offer them!

#1. Apache Cassandra

Apache Cassandra is a high performance, free and open-source distributed NoSQL database management system. It follows a peer-to-peer architecture and is designed to handle large amounts of data across many commodity servers. Cassandra is a highly fault-tolerant DBMS with no single point of failure. This allows for the ‘high-availability’ of the data, as it is ‘replicated’ across the network servers.

Significance of Apache Cassandra;

There must be a crucial reason as to why companies like Reddit, Facebook, Netflix, etc. extensively use Cassandra. Besides offering a real-time performance and being open-source, Apache Cassandra offers ‘elastic-scalability.’ This means any number of nodes can be added or deleted from a cluster. This does not require changing query applications or a ‘cluster reboot.’ 

Moreover, Cassandra efficiently harnesses the processing power of several multicore machines to operate on large datasets.

How can Apache Cassandra help you?

There is a high demand for skilled professionals who can build and scale new infrastructure, platforms, and processes for an organization. Apache Cassandra offers you with relevant skills to undertake these operations.

#2. Apache Hadoop

Apache Hadoop is another open-source framework that uses the MapReduce programming model for distributed storage and processing of large datasets. It is based on a network of ‘computer-clusters’ built from the commodity hardware. Extensive usage by companies like Google, Amazon, etc. has made it the central store for Big data in the businesses. Thanks to it, even small-scale companies can now apply data-science to tackle various business problems.

Significance of Apache Hadoop;

Hadoop allows for running data exploration and analysis on full-datasets, without the need for sampling them. Writing a simple script on Hive or PIG will let you launch the MapReduce job directly on Hadoop over full-datasets. Also, Hadoop is essential mining massive datasets, essential for outlier detection, clustering, and recommendation engines. No wonder why it is vastly employed by e-commerce companies worldwide. 

How can Apache Hadoop help you?

Besides the above-listed features, Hadoop is an excellent tool for large-scale ‘pre-processing’ of raw data. As a data science professional, your major time will be consumed in ELT (Extraction, Loading, and transformation of the data), cleaning and feature spotting in the data. Rest is just running it through the algorithms for predictions. Practical knowledge of this tool will let the companies know that you are here for their rescue, by undertaking this crucial and time-consuming process!

#3. Neo4j

Neo4j is an ACID (Atomicity, Consistency, Isolation & Durability) compliant transactional database, with integrated ‘graph database management,’ graph storage, and processing. Neo4j is used to find relationships and make sense of the complete data set puzzle.  This allows the data scientists to navigate the connections between the nodes in constant time. In a relational database, where many JOIN-SQL-queries slow down the system, Neo4j gives you an edge. 

Significance of Neo4j;

As you may know, data cleaning and munging consume a majority of time than ‘actual-model-building.’ Sorting queries in huge volumes pose another challenge. These obstacles hinder the progress of a project, especially when they are running on collaborative efforts. Neo4j eases these tasks. It provides you with the ability to store unstructured and highly-connected data. It's feature like ‘Cypher’ cut on data munging time and traversing the ‘tree structure’ datasets. Moreover, when the data queries increase exponentially, Neo4j’s performance remains constant. Therefore, it is a better alternative to relational and non-relational databases like SQL and NoSQL.

How can Neo4j help you?

Neo4j is the latest and most sought-after tool in graphic database management. It helps the professionals operating on Big data to save time and complete the projects without much hassle. If you possess expertise in Neo4j, companies are looking out for people like you! 

#4. Cloudera

Cloudera offers a data science workbench is a tool for easy, fast, and secure ‘self-service’ data science for the enterprises. With Cloudera, one can utilize his/her existing skills and tools (Such as Python, R or Scala) to run heavy computations on the Hadoop clusters securely. In short, it enables the data science teams to develop machine learning workloads in their language on convenience rapidly. Users can even request ‘schedulable’ resources like processing power, memory, GPUs, etc. on a centrally managed shared-data cluster.

Significance or Cloudera Data Science Workbench (CDSW);

Cloudera has allowed the enterprises to build ML scale-models to crunch data sets (Through Cloudera Altus). It acts as an interactive medium for building data pipelines on a cluster. Cloudera is one cost-effective solution for processing large-scale datasets in the cloud. 

How can Cloudera help you?

Enterprises are engaged in crunching their datasets and using predictive analytics for business intelligence. It would add to their business value if you can offer your expertise and skills of working on Cloudera platform. Learn this tool and boost your chances of being a developer or a data analyst in various companies.

#5. Apache Solr

Apache Solr is a JAVA based search platform to search the data stored in Hadoop Distributed File System (HDFS). It is used for quickly searching the data regardless of its form, i.e., location, text, tables, etc. Apache Solr combines broad administration interfaces with real-time ‘indexing speeds’ to accelerate the search process. It is a high fault tolerant (No single point of failure!) system and highly scalable.

Significance of Apache Solr;

Solr is used for information retrieval with a full-text search from the HDFS clusters. It includes features like spelling checks, highlighting, autocomplete query, etc. Solr recognizes multiple formats of data and avoids their duplication. It is mostly used by the enterprises to index documents and retrieving them again. It has also been used to integrate ‘auto-suggestions’ in the search engines, the creation of digital libraries and enabling inbox search for social media platforms like the Facebook.

How can Apache Solr help you?

Since it is a search engine as well as a distributed document database, Solr’s use cases are numerous. Combine it with other tools, and you get something very usable. Apache Solr’s versatility helps the enterprise create digital libraries and arrange their documents safely. The practical knowledge of this tool will compliment your skills and impress the big data companies. 

#6. Apache Spark

Apache Spark allows accessing of data from Hadoop, Cassandra, etc. It is an open-source engine that runs atop the Apache Hadoop or the cloud network. Apache Spark is explicitly designed to handle ‘big data’ and the sophisticated analytics associated with it. It is based on Scala and comes with a machine-learning library (MLlib).

Significance of Apache Spark;

Apache Spark is a faster cluster-computing framework. It saves the data in RDDs (Resilient Distributed Datasets) for quick access. Apache Spark also allows to capture and analyze ‘high-velocity’ and ‘high-volume’ data in real-time. It also enables the data science teams to run graph processing to capture relationships between various data-points (or entities). 

Think of it as a versatile tool that offers iterative processing, speed and rapid access to the data-clusters. Spark has been used in building fraud detection applications, log processing for live-stream websites, etc.

How can Apache Spark help you?

Since Spark meets many potential needs at once, it is widely used by the organizations. It runs above the Hadoop system, and your familiarity with Hadoop will ease your way into learning Apache Spark. Companies use Spark for batch and stream processing of the real-time data for efficient application development, deployment, and maintenance. Learning Spark will let your resume look striking to the relevant companies.

#7. Apache Flink

Apache Flink is an open-source framework written in Java and Scala, developed by the Apache software foundation. It is used for scalable streaming and batch data processing (hybrid platform). Flink sorts random data flow programs in a ‘data-parallel’ and ‘pipeline’ sequence. Hence, you can process ‘low-latency’ data almost instantaneously, so that the analysis and decision making are not delayed. Apache Flink also supports machine learning and graph processing.

Significance of Apache Flink;

Flink has powered the companies to make real-time decisions based on inflowing streaming data. It has also made a mark in several use cases of real-time information processing. For example, Apache Flink is used to optimize e-commerce results in real-time for Alibaba. The inventory information and product details are updated in real-time on this largest B2B website. Another space Flink shows its magic is the network monitoring and fault detection. For example, telecom operators in France use it to monitor their wired and wireless networks. It can be easily speculated that Flink offers many other business use cases by complementing Business Intelligence processes (BI).

How can Apache Flink help you?

Apache Flink is explicitly used for business intelligence data analytics by the companies. Since it speeds up the decision-making process, companies can deliver faster to their target audience. Knowledge of Apache Flink can land you a big data job at e-commerce, Business analytics, and companies.

#8. Apache NiFi

Apache NiFi is an open-source ‘data logistics’ framework for facilitating the flow of data across systems. It is written in Java and employs ‘flow-based’ programming for real-time automated data flow management. NiFi allows for effective-load spreading, handling extreme transaction rates and copy-on-write data feature. This is achieved through content-repository and write-ahead log applications integrated with Apache NiFi. Apache NiFi prioritizes the data queues, buffers the data (within the data queues) and allows to create flow-specific configurations (in case of critical and fault-intolerant data).

Significance of Apache NiFi;

Data flow between various systems and DBMS is a challenging task. It often witnesses disk fails, the network fails, and software crashes. Also, sometimes the rate of data access can outrun the rate of consumption. This can hamper the data-delivery chain. Apache NiFi enters the scenario with a solution. It provides a single interface for all the data flow operations, with cross-domain, cross-OS and cross-protocol data transfers. The flow-based-programming architecture of NiFi has allowed the enterprises to transfer their valuable data between multiple databases without failure easily.

How can Apache NiFi help you?

Data transfer is a necessary operation in any given system architecture. Companies now involve in such operations regularly. Surely, they are continually looking out for experts who can handle their critical datasets across various systems. Expertise in Apache NiFi can add this value to your credentials.

#9. Apache Kafka

Apache Kafka is an open-source stream processing platform written in Java and Scala. It provides a high-throughput (High volume and high speed of data flow) on a unified low-latency platform. Apache Kafka is used for real-time handling of data feeds with reliability and robustness. It acts as a ‘publish-subscribe’ messaging system that maintains a feed of messages topic-wise. Here, ‘messages’ are nothing but ‘byte-arrays’ which can be used to store data objects in any format (Avro, JSON,etc.). 

Significance of Apache Kafka;

In Big data analysis, Kafka is used in the ‘extraction’ step of ETL operation. Extraction step is nothing but ‘sourcing’ of data from multiple sources. Since Kafka classifies the messages under various classes (or topics), the extraction becomes easier. 

Moreover, Kafka is used for ‘flagging’ transactions in real-time. For example, exchange of virtual entities among players of an online game. Kafka monitors for any unfair trade or suspicious activity by the players logged-in through multiple accounts. Besides this, Apache Kafka is used in website activity tracking, operational metrics and log aggregation of the data. 

How can Apache Kafka help you?

Kafka is a crucial tool for data extraction, classification, and its retrieval. It is heavily used by the gaming companies and those with huge active online users. If you think your career lies here, list Apache Kafka in your resume for success!

#10. Apache Samza

Apache Samza is an open-source, near-real-time asynchronous computational framework, written in Java and Scala. The design objective of Samza is to enhance the capabilities of Apache Kafka. Samza uses Kafka for messaging and Hadoop YARN (Yet Another Resource Negotiator) for fault tolerance. In short, it can be called a distributed stream processing framework. It contains features like simple API (Application Programming Interface), fault tolerance, extensibility, processor isolation, etc. Apache Samza can run with other messaging systems with a pluggable API. 

Significance of Apache Samza;

Samza locates storage and processing of batch data on the same machine. This allows for efficient work in a condition where the data won’t fit in the memory. Moreover, Apache Samza is being used by LinkedIn and many other companies. For example, in a LinkedIn data center, the most massive Samza task is to process one-million messages per second during peak online traffic.

How can Apache Samza help you?

Companies often come across scenarios where they have to process many Gbs of data per partition. Apache Samza comes in handy for such tasks. It allows to add/remove various codebases without much ‘ripple’ effects. Companies like Intuit, Quantiply, and LinkedIn are already exploiting Samza. So why not learn it?

#11. Google Cloud dataflow

Google Cloud Dataflow is a cloud-based data processing service that allows for real-time data streaming and batch applications. Developers can set up processing pipelines for the preparation and integration of large datasets for analysis. Google Cloud Dataflow is a ‘serverless’ data processing. This means the data stack is not under central administration but scales automatically.

Significance of Google Cloud Dataflow;

Google Cloud Dataflow allows you to focus on data insights. The enterprises can save time on database administration. Secondly, it allows for ‘infinite’ scalability according to the needs of a company. That means you pay only for what you use. Google Cloud Dataflow is used for sales and segmentation analysis, fraud detection in the financial sector, IoT analytics in manufacturing and for personalized user experience in gaming.

How can Google Cloud dataflow help you?

Google Cloudflow data flow is already popular among small and medium enterprises to handle their data analytics. Detailed familiarity with this platform will make your resume shine.

#12. MongoDB

MongoDB is an open-source document-oriented database program. It is cross-platform which is classified as a NoSQL database program. MongoDB uses JSON like schemas and documents. 

Significance of MongoDB;

MongoDB allows for higher availability, horizontal scaling and faster data access through a ‘relaxed’ consistency model. It is already powering Big data as one of the rapidly-growing databases. You must know that India’s unique identification project ‘Aadhar’ uses MongoDB as one of its crucial databases. Shutterfly, an online photo-sharing community uses MongoDB to manage its 10,000 operations per second!

How can MongoDB help you?

If you hold a knowledge of tools like MongoDB, you are in for some great opportunities. Companies like MetLife and eBay are increasingly migrating towards its usage. The demand for this tool’s experts will boost in the future.

#13. Apache Drill

Drill is an open-source software framework developed by Apache software foundation for the interactive analysis of large datasets. Apache Drill supports data-intensive distributed applications too. It is known for its simplicity of use for analyzing the Big data. Apache Drill acts as a query engine for providing secure and interactive SQL analytics. And, this is done at the scale of petabytes. 

Significance of Apache Drill;

With Apache Drill, SQL analysts and BI specialists can query and analyze the data quickly. This cuts the need for the IT department to prepare the data beforehand. Unnecessary schema maintenance and ETL cycles can be bypassed or simplified by the enterprises to cut the hassle.

How can Apache Drill help you?

One major benefit of Apache Drill is that it offers significant investment savings on Big data analysis. It has made Big data analysis accessible to more people. You might as well gain some hands-on experience on it to boost your professional skills.

#14. Elasticsearch

Elasticsearch is a Java-based broadly-distributed, open-source and readily-scalable enterprise-grade search engine. It can be accessed through elaborate and extensive API.  Elasticsearch supports data discovery applications and allows for their extremely fast searches. It operates in real-time and is based on Apache Lucene’s architecture. 

Significance of Elasticsearch;

Elasticsearch provides the user with immensely fast search results for complex queries. It achieves this by targeting a search query to indexes instead of searching for the text directly. It also records data-transaction logs on multiple-nodes to minimize the data loss. Elasticsearch stores real-world complex information as JSON documents which are indexed by default. 

How can Elasticsearch help you?

Famous newspaper ‘The Guardian’ and tech giant Dell have already witnessed the robustness of Elasticsearch. It has been extensively used by the e-commerce companies for sales analysis and buyer behavior analysis. Learning to use this tool will help you make an excellent impression on the hiring manager!

#15. Apache Oozie

Simply put, Apache Oozie is a server-based workflow scheduling system, used to manage Hadoop jobs. It is based on Java. Workflows in Oozie are represented on the ‘acyclic’ graphs. They are defined as a collection of ‘action nodes’ and ‘control flow,’ represented on that graph. Oozie combines multiple tasks sequentially into a ‘logical work unit.’ 

Significance of Apache Oozie;

Oozie is capable of running several identical Workflow jobs concurrently. It can also be extended to support the additional type of actions. Data science professionals use Oozie to build and manage complex dependent data pipelines.

How can Apache Oozie help you?

The infamous ‘Yahoo’ uses the power of Apache Oozie to manage the streaming data flows. It is also employed by several companies for advertisement, content optimization, and data intelligence gathering. Why not add this tool to your skill set and be versatile?

Big data is the next big ‘digital gold rush.’ Your familiarity and experience with these tools and technologies will carve a hardcore data scientist out of you! Start mastering these and you will land a Big data job soon.

You think we should add some more ‘must-know’ tools to this list? Let us know in the comment box below!