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An Introduction to Big Data Engineering

Curious about big data engineering? Learn about the core capabilities of data engineering and how it benefits your business.

What is Big Data Engineering?

Data engineering is the process of designing, automating, and operationalizing systems that manage data flows and data access. Data engineering typically involves multiple steps:

  • Discovering data from multiple sources
  • Ingesting both batch and real-time/streaming data
  • Transforming and processing data at scale
  • Ensuring data quality and trust
  • Masking sensitive data
  • Delivering data in the desired format to the authorized recipient
 Man at a laptop using big data engineering and self-service analytics tools

The end result is clean, trusted, high-quality, relevant data, delivered to data consumers in ways that let them generate faster, more accurate business insights.

Most of today’s businesses and industries need technologies such as Spark and Kafka, serverless architecture, cloud data lakes, and cloud data warehousing to manage an enormous volume, velocity, and variety of data. Therefore, all modern data engineering is implicitly big data engineering.

 

5 Core Capabilities of Big Data Engineering

Big data engineering integration

To build and manage data pipelines quickly and flexibly at scale, both in the cloud and on premises, you need data that is fully integrated and ready for use. Your data engineering solution should automatically scale in cloud environments, ingest large amounts of data from multiple sources at high speed, build data transformation logic quickly and easily, and parse and profile data automatically.

Big data engineering quality

Data science needs data that’s trusted and relevant—and of high quality—to fuel today’s complex analytics. The key is scalable, role-based data quality managed on Spark or Hadoop, in the cloud, or in on-premises environments. Look for a solution with out-of-the-box templates for detecting and resolving anomalies and for discovering data structure and context, as well as a codeless interface for creating business rules so you can cleanse, standardize, and enrich any data at scale.

Big data engineering streaming

IoT and other streaming data sources are a primary source of big data. They also hold insights critical to initiatives that depend on predictive and real-time analytics such as fraud detection. You can maintain service levels, support AI-powered decision-making, and leverage edge computing for low latency with codeless IoT and stream processing. Choose a data engineering solution that can ingest, process, and analyze data in real time with advanced streaming data transformations and enhanced connectivity for cloud and on-premise source and destination systems.

Enterprise data preparation

The faster you can democratize and operationalize data preparation, the faster you get insights. The right data engineering solution will empower your data engineers and data analysts to discover, enrich, cleanse, and govern data pipelines with minimal IT intervention. This requires AI-powered data preparation, end-to-end data lineage, intelligent data masking capabilities, and the ability to automate and record data prep processes for reuse—all at enterprise scale.

Big data engineering masking

In today’s business world, it’s imperative that you protect sensitive, private, and confidential information from unauthorized access and disclosure while still making it fit for use in business intelligence, application testing, and outsourcing. That requires a data engineering solution with precise controls that align with data privacy laws. Look for the ability to mask, de-identify, and anonymize sensitive data depending on need; seamless integration in heterogeneous environments; and high-throughput, low-latency performance that doesn’t interfere with user experience even at scale.

 

How Different Industries Use Big Data Engineering

Healthcare

Healthcare providers and insurers alike must combine and analyze vast amounts of data from diagnostics, treatment documentation, billing and claims, supply records, and other sources to improve quality of care while controlling costs. Big data engineering connects data across systems to reveal patterns that boost compliance with doctors’ orders, detect fraud, plan staffing levels, allocate resources, and achieve better outcomes for both their patients and their business.

Financial Services

Banks, investment companies, and other financial services firms rely on data to detect fraud, assess risk for credit and loans, predict market movements, evaluate investments, and manage billions of transactions and interactions each day. Big data engineering gives them deeper insight into customer needs and behavior to ensure that they can compete on customer service, connect customers to the most appropriate information and products, and protect customer accounts and sensitive data effectively.

Marketing

Superior customer experiences that boost the top line require lots of data, carefully analyzed to determine whom to target and how best to personalize the interaction. Big data engineering is key to breaking customer information out of silos in real time to optimize its value: developing just-in-time offers, predicting next best actions, measuring campaign effectiveness, and analyzing churn to improve customer retention.

Automotive

The auto industry and related organizations are increasingly using telecommunication and monitoring systems, GPS diagnostics, and other telematics to gather data for vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-everything interactions. Big data engineering enables streaming analytics for real-time diagnostics and efficient traffic management. It also provides data scientists with the reliable information they need to develop algorithms to drive everything from personalized insurance underwriting to research and development of new vehicle capabilities.

Pharmaceuticals and Life Sciences

Data is almost literally the beating heart of an industry frequent project launches, complex drug trials, and stringent regulatory requirements. Big data engineering accelerates the process of ingesting, standardizing, cataloging, and governing data from multiple disparate sources so researchers, business leaders, and other data consumers can easily discover and access the data they need.

 

Customer Success Stories

Shire Pharmaceuticals

Shire Pharmaceuticals part of Takeda, is a global biotechnology firm that develops therapies for rare diseases and highly specialized conditions. Big data engineering speeds integration and preparation of all of its enterprise data so its research and development team can analyze more data more easily and get breakthrough therapies to market faster.

AXA XL

AXA XL is a global insurance and reinsurance agency with more than 100 offices on six continents. Big data engineering reduces the complexity and cost of managing, cleansing, and democratizing big data across business units. As a result, the firm has deeper insights into policyholder, broker, and product performance, which lets it identify more opportunities for brokers and partners to cross-sell and upsell policies.

Avis Budget Group

Avis Budget Group is a global leader in car rental, with a fleet of 650,000 vehicles in 180 countries. Big data engineering allows it to ingest and integrate thousands of fields of data from GPS units, navigation systems, and other sensors worldwide in real time to document assets, monitor demand, predict maintenance needs, and identify potential theft and fraud events.

 

Getting Started with Big Data Engineering

Informatica offers the big data engineering solutions you need to find, ingest, process, and prepare data for AI and analytics:

Find

Discover, curate, and democratize trusted data at scale, across cloud and on-premises. Informatica lets you give data consumers a better understanding of data and its lineage and dependencies so they choose the right data for self-service analytics.

Ingest

Move data efficiently from databases, files, and streaming sources to cloud data warehouses and data lakes and make it available for real-time processing so your decisions are always based on the most current, consistent data.

Process

Build advanced integrations and run them at scale while quickly identifying, fixing, and monitoring data quality and data privacy across all your business applications.

Prepare

Provide data engineers and data analysts with collaborative workspaces and curation processes to find, prepare, and govern data so it takes less time to make trusted, governed, high-quality data widely available for self-service.

Find out more about our comprehensive data engineering portfolio.

 

More Big Data Engineering Resources

Infographic: The Value of End-to-End Data Engineering

Blog: The Rise of Big Data Engineering

Analyst Report: AI/ML-Powered Data Management Enables Digital Transformation

Blog: How Enterprise Data Preparation Empowers DataOps Teams

Article: Big Data Use Cases for 2020 and Beyond

Blog: Real-Time Analytics Drives Strategic Decisions

Blog: Streaming Analytics: What It Is and How It Benefits Your Business