SNAP for AdTech

Advertising data is high volume data, where the DataLake or warehouse can quickly grow to terabyte scale. Further, query response times have to be sub-seconds, supporting hundreds of concurrent users.

SNAP is designed to provide 100ms response times for point queries on large datasets. SNAP’s in-memory datamart can be accessed as a “Query as a Service” model or from tools like Tableau.

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Learn how a large AdTech company uses SNAP for web. mobile and video analytics

SNAP For Corporate Performance Management

As data volumes increase, having timely visibility to your operations and finance data gets more and more difficult. Data from Oracle ERP and SAP systems pour into your legacy data warehouses. However, analysts have their hands tied, unable to access data in a timely manner for insights.

SNAP lets your analysts and data scientists analyze business operational data, using the tools they are already familiar with.

Unlike legacy technologies, SNAP does not create extracts, summary tables or pre-aggregated cubes. SNAP enables in-depth access to full detail data for slice and dice analysis and machine learning without compromising response times.

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Learn how a Fortune 10 conglomerate uses SNAP for ERP data analytics

SNAP Predictive Analysis

Business data is not just for reporting. Analysts often have to plan supply to meet demand and for this forecasting is key. Traditional ways of running forecasts involved investing in expensive proprietary tools. With the advance of data analysis and ubiquitous Python, SNAP now provides a way for SQL users to combine the power of Python with the scale out power of SNAP.

Build your forecasting applications on SNAP/Spark and reduce cost by moving away from proprietary databases and software.

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Learn how to leverage SNAP and Spark for machine learning and forecasting with SparklineData Partners