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The Fundamentals of Big Data Integration

Get an overview of big data integration, including what it is, the most common sources of big data, and 5 best practices for building big data pipelines.

Vish Margasahayam StreamSets
By and Posted in Data Integration December 14, 2023

Data is big, and it’s only getting bigger. In a recent study, data professionals reported a 63% data volume growth monthly and an average of over 400 data sources for feeding their BI and analytics systems. With such diverse and numerous data sources, manual collection, integration, and processing methods fall short.

However, big data integration can be complex and challenging due to its voluminous, complex, and varied nature, which calls for a carefully planned integration strategy.

This article explores big data integration, including the most common sources of big data, how these data sources add to the complexity of big data integration, and some best practices to employ as you design your big data integration pipelines.

What Is Big Data Integration?

Big data integration refers to gathering and collecting data from multiple data sources like IoT devices, social media, customer and business systems to create a single, robust data set for running analytics and business intelligence efforts. Big data integration can be complex as it deals with fast-flowing, sometimes transient, and voluminous data arriving in various types and formats.

The main characteristics of big data make the process all the more challenging:

  • Volume: Big data is massive, measuring up to petabytes and exabytes of data and continues to grow. This affects your integration strategy and choice of tools and technologies. For example, big data requires massive storage capacity and compute needs, and your chosen tools must employ seamless scaling to accommodate growing demands.
  • Variety: Data flowing from big data sources rarely arrive in a single format and type but as a mix of structured, unstructured, and raw data. Integrating this data without appropriate cleaning and validation steps introduces unreliable and dirty data into your analytics pipeline, which produces inaccurate analysis.
  • Velocity: The existence of multiple data sources means fast and continuous data generation for analytics. Your integration strategy depends on the need for analytics, e.g., transient data calls for real-time analysis to act on data before it loses its relevance/value.
  • Veracity: Not all data generated from your data sources is valuable. Big data integration must use ETL/ELT processes and other integration techniques to extract and process data to remove irrelevant and bad data to ensure only high-quality is available for analysis.

A successful and effective big data integration process requires a careful blend of skilled talents, integration design, and tools and technologies to cater to the challenging process and often combines real-time and ETL processing techniques to serve business needs like real-time information delivery and business intelligence.

The Most Common Sources of Big Data

Big Data draws its data from multiple sources, which can be classified into three main groups:

  • Machine data: This source generates data at regular intervals or on the occurrence of an event. This data may be from application server logs, user applications like health applications, or cloud applications. Machine data encompasses sources like IoT devices like wearables, mobile devices and desktops, traffic cameras, sensors, and logs from industrial equipment, satellites, and many more. Machine data responds to changes in external factors and often requires real-time analytics to respond to changes.
  • Social data: One of the biggest big data contributors is data generated by social media websites like Facebook, Instagram, X (formerly Twitter), and other social media platforms. This data comes in the form of photos, video, audio, message exchanges, and comments. For context on how much social media contributes, Facebook currently has over 2 billion active users; over 1.7 million pieces of content are shared every minute on Facebook, and over 2.43 million snaps are sent over Snapchat each minute. However, the complexity and variety of social media data makes it challenging to integrate with data from other sources.
  • Transactional data: Transactional data captures the data generated during any transaction. Data generated may include time of transaction, products purchased, invoice number, product prices, discount information, payment methods, and other data. Transactional data has multiple touchpoints and hence generates highly unstructured data containing numbers, letters, and symbols.

Integrating these data sources can be challenging due to the heterogeneous nature of the data. One must address the dirty and fast-flowing nature of these data arriving from multiple locations and adopt an integration strategy that controls the flow of the data while ensuring the security and quality of the data.

For example, although social media data provides value in helping businesses understand their customers better, it is often unstructured, noisy, and contains a lot of biased and irrelevant information arising from spam and fake accounts, bots, or trolls. An efficient data integration strategy must employ appropriate tools to clean, filter, and standardize the data before integrating with other sources to ensure quality and reliable analytics.

Furthermore, these data sources often contain sensitive personal information; transactional data contains credit card information; and machine data, like medical wearables, often contains health data. This means you need standard data governance policies to ensure data privacy and protection as the data flows during the integration.

Best Practices for Building Big Data Pipelines with StreamSets

Building data pipelines with big data integration tools goes more smoothly when you follow best practices. Here are 5 we suggest:

  1. Design your data pipelines for simplicity.
  2. Use labels and naming conventions to easily keep track of what pipelines and processors are doing.
  3. Check in pipelines after each significant change and craft short and clear commit messages with titles that will help you select the correct draft in the event that a rollback is needed.
  4. Test pipelines and processors often.
  5. Use data parameters wisely.

Check out this previous article to dive into these more deeply.

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