Teradata announces a new ‘design pattern’ approach for data lake deployment which leverages years of experience in big data consulting and optimization to help clients build and benefit from data lakes. The new design pattern approach is claimed to be an industry first in helping business users, data scientists and IT professionals establish data lakes that produce exceptional business value.
The company reckons that organizations are exploring the functionality of data lakes to create insight and opportunity from exploding data volumes, yet serious problems entangle their IT teams, including a lack of best practices, the shortage of data scientists, and even confusion regarding the definition of a data lake.
In addition to these challenges, technology choices are multiplying. For example, data lakes are typically assumed synonymous with Hadoop – which is an excellent choice for many data lake workloads – however, a data lake can be built on multiple technologies such as Hadoop, NoSQL, Amazon Simple Storage Service (S3), a relational database (RDBMS), or various combinations thereof. And while technologies are critical to the outcome, a successful data lake needs a plan. A data lake design pattern is that plan. The design pattern consists of intellectual property based on enterprise-class best practices combined with products co-developed from a stream of successful customer engagements.
“In today’s competitive environment, data lakes can provide practical benefits to Indian companies who are increasingly adopting big data analytics. Data lake design pattern is the way forward today and Teradata’s expertise will help in providing great value to businesses while simplifying business infrastructure,” said Sunil Jose, Managing Director, Teradata India.
“Teradata has moved ahead of the curve in defining implementation patterns for data lakes,” said Tony Baer, Senior Analyst, OVUM. “A data lake is different from an operational data store. Teradata’s value proposition stems from practical, on-the-ground experience helping customers cope with managing data in heterogeneous environments. With the acquisition of Think Big, Teradata has added valuable IP — design patterns that will help build transparent data lakes.”
By having access to new data such as customer service records, clickstream data, IP traffic, log information, and sensor data stored in a data lake, users can address cases that generally require multiple, simultaneous interpretations of the data to be tested against each other. A couple of use cases include:
- Generating improved customer churn detection models by extracting text from customer service calls stored in the data lake, then applying predictive text analytics methods.
- Providing for trend analytics against combinations of vast streams of machine data with consumer data. In the utility industry, for example, data lakes pave the way for running multiple data models against each other to examine the impact of installing energy-efficient appliances and the latent effect, months later, of reducing electricity consumption.
“Who hasn’t heard about data lake implementation nightmares? This is why we’re growing: we are asked to step in and help companies turn around ugly, costly data lake failures,” said Ron Bodkin, president of Think Big, a Teradata company. “We tailor our data lake design pattern approach to each set of circumstances – and these patterns and supporting software frameworks are strong, proven value accelerators. Sadly, so many companies find big data landmines the hard way. We get customers out of crisis mode and help business, IT and data scientists plan, execute and benefit from data lakes that actually generate great business value – as they should and will, when built from experience.”