Simple data analytics can sometimes seem like a misnomer. Technology that’s simple for engineers and IT may add complexity to business processes. And the inverse can be true for implementing agile analytics into traditional Business Intelligence (BI) systems. We often hear from customers in industries--ranging from retail to hospitality--that highly skilled teams struggle to deliver what the organization really needs: actionable and data-driven business insights.
Today we’ve added some new capabilities to Google BigQuery that will give your business new ways to work effectively with large amounts of data.
- Big JOIN: use SQL-like queries to join very large datasets at interactive speeds
- Big Group Aggregations: perform groupings on large numbers of distinct values
- Timestamp: native support for importing and querying Timestamp data
With these capabilities, you will now be able to join and perform aggregate analysis on multi-terabyte datasets using SQL-like queries or integrated 3rd party tools, instead of having to initiate complex coding projects.
We’ve been using this technology within Google. For example, when our App Engine team needed to reconcile app billing and usage information, Big JOIN allowed the team to merge 2TB of usage data with 10GB of configuration data in 60 seconds. Big Group Aggregations enabled them to immediately segment those results by customer. Using the integrated Tableau client the team was able to quickly visualize and detect some unexpected trends.
Pricing remains the same: you pay only for the actual data that’s processed by your queries.
Joining terabyte-sized tables has traditionally been a challenging task for data analysts, requiring sophisticated MapReduce development skills, powerful hardware, or a lot of time--often all three. Today with BigQuery you can get directly to business insights using SQL-like queries, with far less effort and far greater speed than you could before.
For those interested in learning more, we’ve also provided technical details in our Developer Blog.
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