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.

1. 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.

2. 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.

3. 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

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.

Snowflake

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

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

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.

Vertica

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.

Greenplum

Born out of the open-source Greenplum Database project, this platform uses PostgreSQL to conquer varied data analysis and operations projects, from quests for business intelligence to deep learning. Greenplum can parse data housed in clouds and servers, as well as container orchestration systems. Additionally, it comes with a built-in toolkit of extensions for location-based analysis, document extraction and multi-node analysis.

IBM Cloud

IBM’s full-stack cloud platform comes with 170 built-in tools, including many for customisable big data management. Users can opt for a NoSQL or SQL database, or store their data as JSON documents, among other database designs. The platform can also run in-memory analysis and integrate open-source tools like Apache Spark.

Pivotal

The Pivotal Big Data Suite is an integrated solution that enables big data management and analytics for enterprises. It includes Greenplum, a business-ready data warehouse, GemFire, an in-memory data grid and Postgres which helps deploy clusters of the PostgreSQL database. With a data architecture built for batch as well as streaming analytics, it can be deployed on-premise and in the cloud, and as part of Pivotal Cloud foundry.

Hevo

Hevo is a Fully Automated, No-code Data Pipeline Platform that helps organisations leverage data effortlessly. Hevo’s End-to-End Data Pipeline platform enables you to easily pull data from all your sources to the warehouse, and run transformations for analytics to generate real-time data-driven business insights. The platform supports 150+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services.

4. What are the essential components/features of Big Data Platform?

• Ability to accommodate new applications and tools depending on the evolving business needs.

• Support several data formats.

• Ability to accommodate large volumes of streaming or at-rest data.

• Have a wide variety of conversion tools to transform data to different preferred formats.

• Capacity to accommodate data at any speed.

• Provide the tools for scouring the data through massive data sets.

• Support linear scaling.

• The ability for quick deployment.

• Have the tools for data analysis and reporting requirements.

What are the Big Data Analytics Use Cases?

• Log analytics

• E-commerce personalisation.

• Recommendation engines.

• Fraud detection.

• Regulatory Reporting for financial and other institutions.

• Automated candidate placement in recruiting.

Now we know what Big-Data engineering is and what are the platforms available to work on. Next, we will get into more specifics about where and how to use this, what are the principles and guidelines etc in the upcoming articles.

References:

https://www.xenonstack.com/blog/big-data-platform

https://builtin.com/big-data/big-data-platform

https://www.selecthub.com/big-data-platform-software/

https://www.accenture.com/us-en/blogs/search-and-content-analytics-blog/big-data-use-cases-business

https://www.projectpro.io/article/5-big-data-use-cases-how-companies-use-big-data/155.