Conventional data processing methods are slow, manual, and expensive
Slow & Stale
Many data engineering teams rely on nightly batch jobs that run every night as part of the ETL process. Often, in addition to ETL and data enrichment steps, these jobs also create smaller summaries that are then shared and used by the internal and external customers throughout the next day. This process, while considered status quo, is obviously tedious, brittle, and can lead to stale and inconsistent data.
Data engineering and BI teams are constantly occupied with speeding up the slow queries and dashboards in order to improve the user experience. One of the most common approaches is to pre-aggregate the data and create various cubes and materialized views. While a pre-aggregated cube or a materialized view could be effective at speeding up a few queries that share the same dimensions or grouping conditions, they are far from ideal. First, manually defining cubes for complex queries is tedious, maintaining them as new data arrives can be costly, and most importantly, the number of cubes or materialized views needed grows linearly with the number of slow queries.
With the rapid growth of data volumes, most organizations are seeing their compute costs sky- rocket. Similarly, as companies become more data-driven, from product management to marketing to customer success, they are making data available to more users. The larger user base means larger compute costs (both for cloud as well as on-prem data warehousing).
Why The World Needs Data Learning
Fast & Real Time
Data Learning summaries the underlying data into a set of lossy and lossless models using state-of-the-art machine learning algorithms. These tiny models, called smart models, are used to produce both exact and approximate answers to queries that would otherwise need to process terabytes of raw data.
Data Learning automates the entire process of 1) deciding what data to summarize and how, 2) maintaining those summaries, and 3) creating new ones as the query patterns or the underlying data change over time.
Invoking a handful of smart tiny models is orders of magnitude more efficient than processing massive volumes of raw data. Not only do queries run faster, they also use significantly fewer computational resources. This means a smaller cluster, and therefore a smaller bill from the cloud and data warehouse providers.
Real World Application of Data Learning
Data Learning relies on sound mathematical concepts that transcend different industries. Any industry that collects, analyzes and acts on data can utilize Data Learning for competitive advantage.
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