How to preprocess data using BigQuery ML.

BigQuery BI Engine is a fast, in-memory analysis system for BigQuery currently processing over 2 billion queries per month and growing. BigQuery has its roots in Google's Dremel system and is a data warehouse built with scalability as a goal. On the other hand BI Engine was envisioned with data analysts in mind and focuses on providing value on Gigabyte to sub-Terabyte datasets, with minimal tuning, for real time analytics and BI purposes.

Uncontrolled cloud costs pose an enormous risk for any organization. The longer these costs go ungoverned, the greater your risk. Volatile, unforeseen expenses eat into profits. Budgets become unstable. Waste and inefficiency go unchecked. Making strategic decisions becomes difficult, if not impossible. Uncertainty reigns.

Arvind Fashions Ltd digital transformation unlocks the value of existing applications, new insights, and build solid workflow through Google Cloud.

The role of the CDO will not suffer the slow rise to prominence other emerging C-level roles have seen in previous years. Modern technologies designed to easily centralize, access, analyze, share, and monetize data have arrived. As a result, data has become the most powerful resource to drive an organization’s transformation. That puts CDOs at the wheel, their foot on the pedal, and their fellow execs holding on tight for the ride of their lives.

Lytics leverages Google BigQuery to offer data infrastructure as a service, and Google Cloud AI/ML stack to identify and create unique audience segments.

Welcome to the BMC Smart Reporting to Yellowfin BI webinar recap! Following our recent webinar for BMC customers held on January 18, 2023, this blog provides a helpful summary for those who could not attend or who would like a quick text recap on the most important points. Speakers at the event included: Missed the event? You can watch the webinar on-demand for free at your convenience. If you prefer to read the highlights, we've got you covered in our handy recap - read on.

Data quality is fairly simple nomenclature to describe the state of the data being processed, analyzed, fed into AI, and more. But this modest little term belies an incredibly critical and complicated reality: that enterprises require the highest level of data quality possible in order to do everything from developing product and business strategies, and engaging with customers, to predicting the weather and finding the fastest delivery routes.

Have you ever considered how much data a single person generates in a day? Every web document, scanned document, email, social media post, and media download? One estimate states that “on average, people will produce 463 exabytes of data per day by 2025.”

The lingering effects of the global pandemic are merging with inflation to create a perfect storm for retailers looking to find the right inventory stature for the seasons ahead. Companies are getting squeezed between rising supply chain costs and falling consumer confidence. To succeed in this volatile market, McKinsey suggests that retailers “accelerate decision-making tenfold.”