Meeting the enterprise’s demand for Big Data technology was a key reason why Teradata was founded. In fact, the company’s name comes from our ability to manage terabytes of data. (Today that number has grown to many petabytes of data!). Back then, storing and analyzing large volumes of data was limited by the hardware available. We created the Teradata Database to effectively manage data at scale to enable enterprise analytics, solving the technical issues and demonstrating the power of integrating data in a relationship model.
Today, as the data warehouse runs tens of millions of queries per day and supports mission-critical operations, the technology challenges have evolved far beyond the confines of hardware. To keep up with the growing volume and diversity of data, the enterprise requires a Big Data architecture that ensures data flows to the right users via the right tools.
The Big Data Architecture Questions To Ask
Designing the architecture you need often begins with asking the right questions:
- How will your Big Data strategies shape your Big Data technology needs? In other words, what are the business problems to be solved, operations to be improved, and objectives to be achieved by leveraging Big Data?
- Which of your existing data sources and systems can be “plugged into” an integrated architecture?
- How will you account for new data sets (like sensor data or data from the Internet of Things)?
- How can your approach help move data-driven and analytics-enabled thinking into the center of your business?
- What are the required components to “operationalize” or scale your Big Data and analytics program beyond pilot phases?
Big Data Technology Must-Haves
In our work with thousands of enterprises from around the world, we’ve identified five critical must-haves when it comes to building Big Data platforms that work. In our view, Big Data architecture must be:
Unifying data warehouses, data lakes, and analytics into a single platform bridges the gap between raw data sources, specific business intelligence tools, and standard CRM applications. This dramatically reduces the complexity of traditional “hybrid” environments and enables companies to ingest extremely fast-moving datasets. It also offers users cross-platform access to data and analytics engines.
According to a recent IDC survey, 56 percent of respondents said that they would reject an IT infrastructure provider or IT cloud services provider outright if the vendor did not offer flexible IT consumption options. In response to requests from our own customers, Teradata developed Vantage, a data analytics software platform, to be consumable in whatever way works best for them, whether across cloud, hybrid, or on-premises. We know that customers may not be able to predict the scale of analytical capabilities they’ll need next month, let alone next year, so we’re giving them the flexibility to adjust their analytics workloads as their deployment strategies shift.
Many analysts and experts say that today your enterprise’s ability to collect and manage Big Data is a foregone conclusion — it’s your ability to predict and act on Big Data that matters. That’s where artificial intelligence and its subset discipline machine learning come in. Machine learning gives computers the ability to learn without manual programming, allowing you to see patterns and build models from your data that predict future scenarios. This can be enormously useful to determine a customer’s propensity to buy your product, optimize the output of every machine in your manufacturing plant, or improve your security posture.
When we developed Vantage, we made sure the platform would support a wide range of advanced analytic functions. For example, the Machine Learning Engine delivers more than 100 prebuilt analytical functions for path, pattern, statistical, and text analytics to address a broad swath of analytics. Likewise, the Graph Engine provides a set of functions that discover relationships between people, products, and processes within a network.
You’ll reduce data siloes and increase the rate of innovation at your enterprise with technology and policies that support self-service analytics. Ideally, people across your organization would have access to the right set of tools and data they need to do their jobs effectively, without having to request permissions from IT. This empowers your people to spend less time stitching together different solutions and more time finding and applying answers to the business’ most critical strategic questions.
Your technology should also support whatever languages and tools your users prefer. With Vantage, data scientists and business analysts can work with the same data, even if they’re using different tools and languages. Vantage integrates with customers’ preferred tools and languages, including SQL, R, Python, Tableau, Qlik and Teradata AppCenter, Jupyter and RStudio.
5. Open to Innovation
Emerging use cases of advanced analytics and the explosion of various types and sources of data are compelling data scientists to leverage varied data science techniques. Your technology should be comprehensive and capable of solving the business problems of both today and tomorrow, so you’ll need an architecture that’s compatible with whatever new tools and technology emerge in the future. That’s why we developed Vantage to combine both open source and commercial analytic technologies together.
The technologies that support Big Data analytics are changing rapidly. In order to create a long-term, sustainable Big Data technology strategy, it’s critical that you build architecture that integrates various data streams, tools, and applications; is scalable depending on the needs of the business; supportive of advanced predictive analytics capabilities; accessible to everyone at your organization, no matter their role; and open to future innovation that will inevitably emerge. With these criteria in mind, you’ll be well-equipped to gain an edge as the global data economy continues to expand.
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