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Fundrise CTO: New SQL Tools Lead to ‘Wonderfully Seamless Solutions’

The Startup Magazine

Despite its decades-old roots, SQL is undergoing a renaissance, buoyed by SQL tool advancements in a variety of data-related tools. What Is SQL? SQL is a standardized programming language specifically designed for managing and interacting with relational databases, where information is stored in interrelated tables.

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A Marketer’s Guide to Kaggle for Analytics and Data Science

ConversionXL

Working with this dataset can be valuable in terms of understanding the underlying structure of Google Analytics data and experimenting with a number of advanced statistical and data mining techniques that can’t be applied when the data is in aggregate form (which is the norm with standard Google Analytics.). Let’s have a closer look.

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Amazon Redshift Too Slow or Crashing? BlazingDB Performs

Austin Startup

Massive Scale with Unprecedented Speed BlazingDB offers a massively distributed, “cloud first”, high performance SQL database that achieves performance gains on the largest workloads and datasets at extraordinarily competitive costs. Transformations, aggregations and joins at massive scale is where BlazingDB wins!

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Scaling lessons learned at Dropbox, part 1

eranki.tumblr.com

Another thing that became increasingly useful was to have thousands of custom stats aggregated over thousands of servers graphed. We eventually ended up converting everything to SQLAlchemy’s lowest-level language for constructing SQL (one step away from raw SQL). Better than switching off features! App-specific metrics.

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Raw Data & Google Analytics: A Game Changer

ConversionXL

After a few hours playing around with SQL , I was already able to deliver insights I never could have with aggregated Google Analytics reports. What’s the difference between raw and aggregated data in Google Analytics? Google Analytics, in the free version, provides only aggregated data. Where do my users come from?

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Google BigQuery: A Tutorial for Marketers

ConversionXL

Alternatives include Amazon Redshift , Snowflake , Microsoft Azure SQL Data Warehouse , Apache Hive , etc. The solution is to give every lead and every purchase a userID (like an encrypted email), to pull CRM and Google Analytics data into your BigQuery data warehouse, and then—with a simple SQL query—join the two tables.

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Google Analytics vs. Google Analytics 360 (Based on a Decade of Implementations)

ConversionXL

For example, while the data aggregation process in Google Analytics seems like a “normal” feature, it might be a hurdle if your business needs to process data at the hit level instead of by sessions or campaigns. An enterprise data warehouse for fast SQL queries. The root of this problem is hidden in the logic of both tools.