The challenges of ever-growing datasets
Organizations have many assets, such as physical assets, intellectual property, the knowledge of their employees, and trade secrets. But for too long, organizations did not fully recognize that they had another extremely valuable asset, and they failed to maximize the use of it—the vast quantities of data that they had gathered over time.
That is not to say that organizations ignored these data assets, but rather, due to the expense and complex nature of storing and managing this data, organizations tended to only keep a subset of data.
Initially, data may have been stored in a single database, but as organizations, and their data requirements, grew, the number of databases exponentially increased. Today, with the modern application development approach of microservices, companies commonly have hundreds, or even thousands, of databases. Faced with many data silos, organizations invested in data warehousing systems that would enable them to ingest data from multiple siloed databases into a central location for analytics. But due to the expense of these systems, there were limitations on how much data could be stored, and some datasets would either be excluded or only aggregate data would be loaded into the data warehouse. Data would also only be kept for a limited period of time as data storage for these systems was expensive, and therefore it was not economical to keep historical data for long periods. There was also a lack of widely available tools and compute power to enable the analysis of extremely large, comprehensive datasets.
As an organization continued to grow, multiple data warehouses and data marts would be implemented for different business units or groups, and organizations still lacked a centralized, single-source-of-truth repository for their data. Organizations were also faced with new types of data, such as semi-structured or even unstructured data, and analyzing these datasets with traditional tooling was a challenge.
As a result, new technologies were invented that were able to better work with very large datasets and different data types. Hadoop was a technology created in the early 2000s at Yahoo as part of a search engine project that wanted to index 1 billion web pages. Over the next few years, Hadoop, and the underlying MapReduce technology, became a popular way for all types of companies to store and process much larger datasets. However, running a Hadoop cluster was a complex and expensive operation requiring specialized skills.
The next evolution for big data processing was the development of Spark (later taken on as an Apache project and now known as Apache Spark), a new processing framework for working with big data. Spark showed significant increases in performance when working with large datasets due to the fact that it did most processing in memory, significantly reducing the amount of reading and writing to and from disks. Today, Apache Spark is often regarded as the gold standard for processing large datasets and is used by a wide array of companies, although there are still a lot of Hadoop MapReduce clusters in production in many companies.
In parallel with the rise of Apache Spark as a popular big data processing tool was the rise of the concept of data lakes—an approach that uses low-cost object storage as a physical storage layer for a variety of data types, provides a central catalog of all the datasets, and makes that data available for processing with a wide variety of tools, including Apache Spark. AWS uses the following definition when talking about data lakes:
You can find this definition here: https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/.
Having looked at how data analytics became an essential tool in organizations, let's now look at the roles that enable maximizing the value of data for a modern organization.
