Big Data Platforms for Data Engineering

Big Data Platforms for Data Engineering
Big Data Platforms for Data Engineering – Big data platforms for data engineering, We discussed in the first article on this series about what Big-Data engineering is and the high-level concepts. In this, we will discuss about the platforms where the data engineering is done, what are the available options, the capabilities/features of any big data platform and the generic use cases of these.

What is a Big Data Platform?

A big data platform is an integrated computing solution that combines numerous software systems, tools, and hardware for big data management. It is a one-stop architecture that solves all the data needs of a business regardless of the volume and size of the data at hand. These products represent a tidal shift in the way organisations capture, store, and process data. Due to their efficiency in data management, enterprises are increasingly adopting big data platforms to gather tons of data and convert them into structured, actionable business insights.

What is the need for a Big Data Platform?

The platform is used by different set of people in the organisation such as and not limited to data engineers to parse, clean, transform, aggregate, and prepare data for analysis. Business users use it to run SQL and NoSQL queries against the platform. Data scientists use it to discover patterns and relationships in large data sets using machine-learning algorithms. Organisations build custom applications on big data platforms to calculate customer loyalty, identify next-best offers, spot process bottlenecks, predict machine failures, monitor the health of core infrastructure, and so on.

What are the best Big Data Platforms?

Below are some of top big data platforms:

Apache Hadoop

Hadoop is an open-source programming architecture and server software. It is employed to store and analyse large data sets very fast with the assistance of thousands of commodity servers in a clustered computing environment

Google Cloud

Google Cloud offers lots of big data management tools, each with its own specialty. BigQuery warehouses petabytes of data in an easily queried format. Dataflow analyzes ongoing data streams and batches of historical data side by side. With Google Data Studio, clients can turn varied data into custom graphics.


Cloudera is a big data platform based on Apache’s Hadoop system. It can handle huge volumes of data. Enterprises regularly store over 50 petabytes in this platform’s Data Warehouse, which handles data such as text, machine logs, and more. Cloudera’s DataFlow also enables real-time data processing.

AWS Redshift

Amazon Redshift is a cloud-based data warehouse service that enables enterprise-level querying for reporting and analytics. It supports an unlimited number of concurrent queries and users through its high-performing Advanced Query Accelerator (AQUA). Scalable as needed, it retrieves information faster through massive parallel processing, columnar storage, compression and replication. Data analysts and developers leverage its machine learning attributes to create, train and deploy Amazon Sagemaker models.


This big data platform acts as a data warehouse for storing, processing, and analysing data. It is designed similarly to a SaaS product. This is because everything about its framework is run and managed in the cloud. It runs fully atop public cloud hosting frameworks and integrates with a new SQL query engine.

Microsoft Azure

Users can analyse data stored on Microsoft’s Cloud platform, Azure, with a broad spectrum of open-source Apache technologies, including Hadoop and Spark. Azure also features a native analytics tool, HDInsight, that streamlines data cluster analysis and integrates seamlessly with Azure’s other data tools.


Talend is an open-source data integration and management platform that enables big data ingestion, transformation and mapping at the enterprise level. The vendor provides cross-network connectivity, data quality and master data management in a single, unified hub — the Data Fabric.


Teradata’s Vantage analytics software works with various public cloud services, but users can also combine it with Teradata Cloud storage. This all-Teradata experience maximises synergy between cloud hardware and Vantage’s machine learning and NewSQL engine capabilities. Teradata Cloud users also enjoy special perks, like flexible pricing.


This software-only SQL data warehouse is storage system-agnostic. That means it can analyse data from cloud services, on-premise servers and any other data storage space. Vertica works quickly thanks to columnar storage, which facilitates the scanning of only relevant data. It offers predictive analytics rooted in machine learning for industries that include finance and marketing.


This platform, which emerged from the open-source Greenplum Database project, makes use of PostgreSQL to conquer a wide variety of data analysis and operations projects, ranging from endeavors to achieve business intelligence to endeavors to learn at a deeper level. Greenplum has the capability to parse data that is stored in containers orchestration systems, as well as clouds and servers. In addition to that, it includes a toolset of built-in extensions that can perform location-based analysis, document extraction, and multi-node analysis.

The IBM Cloud.

The full-stack cloud platform offered by IBM comes equipped with 170 pre-installed capabilities, many of which are geared at adaptable big data handling. Users have the option of storing their information in JSON documents, in addition to traditional database architectures such as NoSQL and SQL databases. In-memory analysis can also be performed on the platform, and open-source technologies such as Apache Spark can be integrated.


The Pivotal Big Data Suite is an integrated system that provides businesses with the capability to handle and analyze large amounts of data. It comes with Greenplum, which is a business-ready data warehouse, GemFire, which is an in-memory data grid, and Postgres, which assists in the deployment of clusters of the PostgreSQL database. It is possible to deploy it on-premise, in the cloud, and as part of Pivotal Cloud foundry thanks to its data architecture, which was designed to support batch analytics as well as streaming analytics.


Hevo is an entirely automated and code-free data pipeline platform that makes it easy for businesses to make use of their data. Through the usage of Hevo’s End-to-End Data Pipeline platform, you will have the ability to effortlessly draw data from all of your sources into the warehouse, as well as execute transformations for analytics to provide real-time data-driven business insights. The platform offers more than 150 ready-to-use connectors with database management systems, software as a service applications, cloud storage services, software development kits, and streaming service providers.

What are the most important aspects and capabilities of the Big Data Platform?

  1. Capability to support the addition of new apps and tools in accordance with the changing requirements of the business.
  2. Support numerous data formats.
  3. the capacity to store or process vast amounts of data either while it is streaming or at rest.
  4. Have available a wide range of conversion tools, so that data can be transformed into a variety of user-preferred forms.
  5. Capacity that can handle data traveling at any speed.
  6. Give users the tools they need to search through enormous data sets and get the information they need.
  7. Give your backing to linear scaling.
  8. The capacity for rapid deployment of resources.
  9. Have access to the necessary tools for conducting data analysis and fulfilling reporting obligations.

What are the Different Use Cases for Big Data Analytics?

  1. Analytical logging
  2. E-commerce customization.
  3. Recommendation engines.
  4. Fraud detection.
  5. Reporting on Regulatory Matters for Financial Institutions and Other Organizations.
  6. The process of applicant placement in recruiting that is automated.

We are now familiar with the concept of Big-Data engineering as well as the various platforms that are at our disposal. In the following articles, we will proceed to go into further information regarding where and how this should be used, as well as the underlying principles and recommendations.