All organizations have data, and in order to understand performance it is important to analyze that data. Many organizations rely on traditional data warehouse and business intelligence solutions to build a one-stop-shop for decision makers to access their reports and data.
The traditional data warehouse approach has done a great job of simplifying data access and reporting as well as combining data from many sources in order to answer all of the questions an organization may have. However, it is impossible to anticipate every question a business might ask and every report they might need. Metrics will change from year to year or month to month…sometimes even day to day! In addition, we have a flood of new types of data flowing into our systems. Data from the web, social media, servers, sensors, documents, comments and devices have caused the volume of data we have to deal with to explode. Fifteen years ago, I certainly never expected to have to keep track of something called a social media “like”!
In a traditional data warehouse solution, we would probably ignore most of these external data sources because they are either too voluminous or in a format that is not easy to manipulate and store. If we used any of it, it was probably for an edge reporting need. These limitations often result in potentially valuable data and insights being inaccessible and possibly lost forever.
In recent years, this data explosion has spawned a new set of technologies and techniques. Apache Hadoop and the Hadoop Data Lake are at the center of the Big Data movement. With all the media hype, it is difficult to sift through the buzzwords and understand where — and even if — these new technologies make sense for your analytics needs. Many people believe that implementing a Hadoop Data Lake means throwing away their investment in a data warehouse. This perception ends up either sending them down the wrong path or causing them to sideline big data as a “future consideration”.
Nothing could be further from the truth! Hadoop, Big Data and the Data Lake don’t replace your existing investment in analytics! In fact, they compliment it very nicely! By building a Modern Data Architecture, you can continue to leverage all of your investments in analytics, you can collect all of that data you have been ignoring or throwing away and you can enable your analysts to get to data and insights faster.
In this short video, I’ll walk you through the differences between a traditional data architecture and our vision of a Modern Data Architecture which includes a Hadoop Data Lake that embraces the enterprise data warehouse and empowers your business analysts and data consumers.