What to do when your Data Warehouse Chokes on Big Data By Pradeep Bhanot August 26, 2020 This may seem like an academic question, but it is increasingly becoming a reality for modern businesses. What do you do when you have millions of records with infinite width and depth, and your data warehouse chokes? Do you trim your data? Do you add more infrastructure capacity? Or do you need to look at a better data warehouse solution? This problem is akin to owning an old car that makes a bunch of noises, smells terrible, and has wheels that rattle when you drive down the road. What do you do about it? Drive slower (that’s annoying), open the windows for some fresh air, and turn up the radio to drown out the sounds? Do you get some new tires, an air freshener, and a louder radio to mask the issues? Or do you consider buying a new car? Nostalgia may be a valid reason to keep a classic car, but it isn’t a good reason to keep a data warehouse around that isn’t meeting your business needs. Your business is evolving, and you need a data warehouse platform that will give you agility and the ability to move faster, not slow you down. Where is the Infinite Data Problem Coming From? The digital transformation of business processes and the rapid adoption of modern connected technology is what is driving the infinite data challenge. Instead of having a business run on a few core platforms with well-structured data schemas and transactional data growth curves that are relatively flat, modern businesses are embracing a wide variety of specialized systems and things like IoT and mobile devices that produce seemingly endless streams of data. This “measure everything” culture, combined with an uptick in data update volume from transactional systems, leads to a data profile where there can be an infinite number of rows of data and a seemingly infinite set of column attributes that are collected. This problem is a sign of success – it means that your organization understands the value of data and is actively working to collect the most diverse and expansive information footprint they can. You don’t want your data warehouse system to get in the way of that. Why is Your Data Warehouse Choking on Big Data? Most data warehouses were designed for on-prem infrastructure hardware with fixed capacity and processing optimized for relational database schemas. This is what companies needed five years ago. Times have changed. Traditional data warehouses are choking because they aren’t architected for big-data analytics in real-time. They aren’t deployed on flexible and scalable cloud infrastructures and configured for on-demand resource scaling, and they are trying to apply old-school scalar processing approaches to modern data structures. If you give the system enough time, it will get the job done, just not with the speed that most modern businesses demand. A Modern Solution to The Big Data Problem Actian Avalanche is a modern solution to your big data problem. Designed for high-efficiency processing, deployed on scalable cloud infrastructure, and leveraging high-performance vectorized data processing, Avalanche can meet the big data challenges of today and give you plenty of growth room for the future. Yes, many other data warehouse solutions can be deployed in the cloud to give you access to the compute and storage capacity, but in a side by side comparison, Actian’s unique approach out-performs the next best option and through highly efficient hardware utilization that can deliver higher performance at a much lower cloud cost. To learn more about how Actian Avalanche delivers superior performance and can cut your cloud data warehouse bill in half, check out this video. To learn more about how Actian Avalanche can help you address your business’s big data problems, visit www.actian.com/avalanche. About Pradeep Bhanot Product Marketing professional, author, father and photographer. Born in Kenya. Lived in England through disco, punk and new romance eras. Moved to California just in time for grunge. Worked with Oracle databases at Oracle Corporation for 13 years. Database Administration for mainframe IBM DB2 and its predecessor SQL/DS at British Telecom and Watson Wyatt. Worked with IBM VSAM at CA Technologies and Serena Software. Microsoft SQL Server powered solutions from 1E and BDNA.