Building End-to-End Big Data Solutions (Part 1)

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5 min read

There's big potential locked away in your big data. If you can tap into it strategically, you can reap countless benefits, from increased productivity to greater profitability. You'll have a deeper understanding of your customers, better visibility into your operations, the insight you need to engage in better planning and decision making, and improved agility to respond to market and competitive shifts.

But many organizations are struggling to realize big data's value, because they're piecing together a bunch of expensive, complicated, and disparate solutions to harness, analyze, and share it. Optimizing your big data assets - and your investment in MapR's Data Platform - requires a well-thought-out strategy. Comprehensive, end-to-end big data environments must:

  • Truly reflect all enterprise information, including both structured and unstructured data from the widest array of sources, to provide the most complete view of your business
  • Ensure that data is fit for purpose - accurate, complete, and consistent - at all times, so it can be exploited for competitive advantage
  • Provide a unified view of big data for all stakeholders, regardless of their role or level of technical expertise
  • Operationalize big data insights, making it easier for business users to identify important patterns and spot critical trends

The right supporting tools are the key to successfully executing your big data strategy. The ultimate goal is to create a fully unified infrastructure that can natively ingest, cleanse, and integrate all your big data (regardless of where it originates or what format it's in), make it work seamlessly with your existing applications and processes, and transform it into valuable insight that can be accessed by all users. Ideally, it must accomplish all this is a way that reduces coding and maintenance, minimizes cost and complexity, and promotes best practices in big data management and analysis.

In this series of posts, we'll take a close look at each of the technologies that an end-to-end big data environment must embrace:

  • Integration. To prevent your big data repository from becoming just another data silo, you'll need robust and full-featured data integration, complete with features for transformation, metadata management, and the ability to share information bi-directionally with cloud-based applications, relational databases, packaged applications, legacy files and systems, and other sources
  • Governance. Your raw data must be refined as it is moved into the MapR Data Platform – generally as part of a streaming or batch ELT process – and must be continuously managed to ensure optimum integrity at all times
  • Visualization. Different types of users need different ways to analyze and visualize big data - scorecards for executives, ad hoc and data discovery tools for analysts and power users, and InfoApps for business users
  • Predictive analytics. In addition to using big data to obtain a historical view of what has happened, you can further extend its value by leveraging it to quickly and accurately predict future events, behaviors, and conditions

It's important to note that many solutions provide functionality that improves the utilization of the MapR Data Platform. But these tools vary in complexity, and serve to accomplish different goals or address different problems. For example, some are command-line driven and some are Linux-based, requiring special expertise. And most call for specific knowledge and/or extensive training. Choose wisely.

In our next post, we'll take a closer look at integration technologies - how they fit into an end-to-end big data strategy, and what features and capabilities you'll need. In the meantime, check out our white paper, Real World Strategies for Big Data.

What does it take to implement a digital transformation? Looking for guidance on becoming a successful data-driven business based on real-world experience instead of opinion? Download the ebook, A_rchitects Guide to Digital Transformation_, compliments of MapR.


This blog post was published February 01, 2017.
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