Concept illustration of complex equations being distilled


Why Teradata

You Need a Simple Answer to a Complex Problem

Analysts are challenged to capture and interpret data that is spread across various systems.

Big data has changed the analytics game. The range of data management and analytics engines continues to expand rapidly. As a result, businesses are forced to use multiple solutions to get the answers they need. Engines offer myriad features at various price points to keep up with organizations integrating big data analytics into their daily operations. Each en­gine is a natural home for particular types of data and analytics. But using multiple engines requires moving and transforming data, along with IT help, to enable businesses to gain insights and value that were previously out of reach.

What organizations need is a simple way to leverage all of their analytics resources. To that end, understanding the capabilities each engine offers is crucial in order to maximize its potential. Plus, new technologies are needed to harness the capabilities of individual engines while cutting through the com­plexity associated with data movement and hetero-geneous interfaces.

So Many Choices

Historically, there has always been a lot of diversity of analytics techniques and options, and it’s no different today. With so many choices and capabilities available, no single platform can do everything on its own. Businesses need multiple platforms, and they have to understand how to easily exploit them.

Although a world with multiple analytics engines is inevitable, analysts should focus on the story data can tell, not where it resides.

The downside to so many options is the inevitable complexity that arises. Data is stored in various analytics systems, with each one handling different types of processing and information.

In addition, many new big data technologies have primitive interfaces and languages that limit end-user adoption, plus the need to integrate results from different engines can bog down operations. This can result in a disjointed and uncoordinated environment that does not deliver the full business capabilities.

Understanding the capabilities each engine offers is crucial to ensuring that organizations derive the full value from their analytics. That is why they need to consider these factors when evaluating alternatives:

It’s Not Complicated Anymore

Organizations are seeking the ability to scale the breadth and sophistication of their analytics to respond to the demands of business operations. The challenge is how to best orchestrate a wide variety of new analytics engines, file systems, storage techniques, procedural languages and data types into one cohesive, interconnected and complementary analytics architecture. This allows analysts to combine business data stored in one location, like the data warehouse, with other types of data in another location to gain insight.

Teradata® QueryGrid™ provides a single interface to leverage all data across the enterprise—wherever it resides—and analyze it on any platform. As a result, the choices companies face on everything from data storage systems to analytics platforms are no longer overwhelming. Instead, companies benefit from multiple solutions by harnessing the unique capabilities of each engine without sacrificing processing time, cost, efficiency or data reliability. Businesses can then embrace and benefit from all of their data.

Teradata QueryGrid eliminates the complexity of the underlying ecosystem and gives analysts access to the data they want, when they want it, regardless of where it is. Other benefits include the ability to:

  • Run the right analytics on the right platform
  • Automate and optimize work distribution through “push-down” processing across platforms
  • Minimize data movement by processing the information where it resides
  • Reduce data duplication
  • Automate analytics processing and data movement between systems with transparency
  • Access data and analytics easily using existing SQL skills and tools

With Teradata QueryGrid, users have seamless, self-service access to data and analytics pro­cessing across different systems from a single Teradata Database or Teradata Aster Database query. This makes data work for the business rather than having business users work to get the data. Teradata QueryGrid allows a single query to reach into the needed data management and analytics platforms to return the best possible result set. The query accesses the data where it’s stored, even if it’s on multiple systems, and uses the processing capabilities of each environment to accomplish the task and get the answer.

Analytics Engines
A wide range of database options is available for performing analytics, including graph, columnar and massively parallel processing (MPP) relational database management systems (RDBMSs). Hybrid models are also available that combine database categories in a variety of ways. For example, some vendors have incorporated columnar and graph technology into MPP databases.

Memory and Disk Types
Engines can be configured with different storage types to achieve faster performance or to minimize costs. They can be configured to have varying amounts of RAM or be designed to process all data in-memory. Spinning disk options are available for high or low I/O and CPU, while more expensive solid-state drives offer very fast data retrieval.

Design Patterns
Analytics engines can support various design patterns, including a data warehouse, data lake or discovery platform. Although technologies and patterns have historically had strongly correlated relationships, that is no longer the case. Instead, vendors have incorporated essential features in their technologies that deliver many key aspects of the design patterns.

Economic Constraints
The revenue potential of analytics capabilities must be evaluated against the costs, including capital expenditures and operational expenses related to development, usage, maintenance, support and data center resources. The wisest course is to pursue value and let the economics of the data and platforms dictate the analytics use. No individual platform is perfect—businesses need more than one solution. The question is how to make exploiting multiple platforms easier.

Pay Attention to the Story, Not the Location

Although a world with multiple analytics engines is inevitable, analysts should focus on the story data can tell, not where it resides. After all, paying analysts to be data movers​—transferring data in and out of different engines—is not a good use of resources.

Analysts should be able to find and access the data they need in a way that allows for ease of use, simplicity and the ability to run whatever analytics are needed. The information should be tapped where it resides to significantly lower the cost and time for analysis.

Organizations are faced with a lot of choices for engines. When selecting them, it’s important to base the decision on design, business needs and budget. 

Dan Woods is CTO and founder of CITO Research. He has written and co-authored more than 20 books about business and technology and has a column on

Your Comment:
Your Rating:

Fuzzy Logix