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Data Warehouse Optimization: Not All Data is Created Equal

Data Warehouse Optimization (DWO) is becoming a popular term that describes how an organization optimizes their data storage and processing for cost and performance while data volumes continue to grow from an ever increasing variety of data sources.

Data warehouses are reaching their capacity much too quickly as the demand for more data and more types of data are forcing IT organizations into very costly upgrades.  Further compounding the problem is that many organizations don’t have a strategy for managing the lifecycle of their data.  It is not uncommon for much of the data in a data warehouse to be unused or infrequently used or that too much compute capacity is consumed by extract-load-transform (ELT) processing.  This is sometimes the result of business requests for one off business reports that are no longer used or staging raw data in the data warehouse.  A large global bank’s data warehouse was exploding with 200TB of data forcing them to consider an upgrade that would cost $20 million.  They discovered that much of the data was no longer being used and could be archived to lower cost storage thereby avoiding the upgrade and saving millions.  This same bank continues to retire data monthly resulting in on-going savings of $2-3 million annually.  A large healthcare insurance company discovered that fewer than 2% of their ELT scripts were consuming 65% of their data warehouse CPU capacity.  This company is now looking at Hadoop as a staging platform to offload the storage of raw data and ELT processing freeing up their data warehouse to support the hundreds of concurrent business users.  A global media & entertainment company saw their data increase by 20x per year and the associated costs increase 3x within 6 months as they on-boarded more data such as web clickstream data from thousands of web sites and in-game telemetry data.

In this era of big data, not all data is created equal with most raw data originating from machine log files, social media, or years of original transaction data considered to be of lower value – at least until it has been prepared and refined for analysis. This raw data should be staged in Hadoop to reduce storage and data preparation costs while the data warehouse capacity should be reserved for refined, curated and frequently used datasets.  Therefore, it’s time to consider optimizing your data warehouse environment to lower costs, increase capacity, optimize performance, and establish an infrastructure that can support growing data volumes from a variety of data sources.  Informatica has a complete solution available for data warehouse optimization.

The first step in the optimization process as illustrated in Figure 1 below is to identify inactive and infrequently used data and ELT performance bottlenecks in the data warehouse.  Step 2 is to offload the data and ELT processing identified in step 1 to Hadoop.  PowerCenter customers have the advantage of Vibe which allows them to map once and deploy anywhere so that ELT processing executed through PowerCenter pushdown capabilities can be converted to ETL processing on Hadoop as part of a simple configuration step during deployment.  Most raw data, such as original transaction data, log files (e.g. Internet clickstream), social media, sensor device, and machine data should be staged in Hadoop as noted in step 3.  Informatica provides near-universal connectivity to all types of data so that you can load data directly into Hadoop.  You can even replicate entire schemas and files into Hadoop, capture just the changes, and stream millions of transactions per second into Hadoop such as machine data.  The Informatica PowerCenter Big Data Edition makes every PowerCenter developer a Hadoop developer without having to learn Hadoop so that all ETL, data integration and data quality can be executed natively on Hadoop using readily available resource skills while increasing productivity up to 5x over hand-coding.  Informatica also provides data discovery and profiling tools on Hadoop to help data science teams collaborate and understand their data.  The final step is to move the resulting high value and frequently used data sets prepared and refined on Hadoop into the data warehouse that supports your enterprise BI and analytics applications.

To get started, Informatica has teamed up with Cloudera to deliver a reference architecture for data warehouse optimization so organizations can lower infrastructure and operational costs, optimize performance and scalability, and ensure enterprise-ready deployments that meet business SLA’s.  To learn more please join the webinar A Big Data Reference Architecture for Data Warehouse Optimization on Tuesday November 19 at 8:00am PST.

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Figure 1:  Process steps for Data Warehouse Optimization

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2 Responses to Data Warehouse Optimization: Not All Data is Created Equal

  1. Tom says:

    ELT -> ETL?

  2. Pingback: Informatica and Cloudera Optimizes the Data Warehouse with an Enterprise Data Hub | Cloudera VISION

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