SQL Archives - aster.cloud

5 min

Sentiment Analysis With BigQuery ML

Introduction We recently announced BigQuery support for sparse features which help users to store and process the sparse features efficiently while working with them. That functionality enables users to represent sparse tensors and train machine learning models directly in the BigQuery environment. Being able to represent sparse tensors is a useful feature because sparse tensors are used extensively in encoding schemes like TF-IDF as part of data pre-processing in NLP applications and for pre-processing images with a lot of dark pixels in computer vision applications. There are numerous applications of sparse features such as text generation and sentiment analysis. In…

3 min

3 Tips To Manage Large Postgres Databases

Try these handy solutions to common problems when dealing with huge databases. The relational database PostgreSQL (also known as Postgres) has grown increasingly popular, and enterprises and public sectors use it across the globe. With this widespread adoption, databases have become larger than ever. At Crunchy Data, we regularly work with databases north of 20TB, and our existing databases continue to grow. My colleague David Christensen and I have gathered some tips about managing a database with huge tables. Big tables Production databases commonly consist of many tables with varying data, sizes, and schemas. It’s common to end up with…

SQL

7 min

Infrastructure from Code: the New Wave of Cloud Infrastructure Management

This overview of date and time values in MySQL will help you take advantage of temporal values in your database tables. Both new and experienced users of the popular MySQL database system can often get confused about how temporal values are handled by the database. Sometimes users don’t bother learning much about temporal value data types. This may be because they think there isn’t much to know about them. A date is a date, right? Well, not always. Taking a few minutes to learn how MySQL stores and displays dates and times is beneficial. Learning how to best take advantage…

7 min

Advancing Cancer Research With Public Imaging Datasets From The National Cancer Institute Imaging Data Commons

Medical imaging offers remarkable opportunities in research for advancing our understanding of cancer, discovering new non-invasive methods for its detection, and improving overall patient care. Advancements in artificial intelligence (AI), in particular, have been key in unlocking our ability to use this imaging data as part of cancer research. Development of AI-powered research approaches, however, requires access to large quantities of high quality imaging data. Sample images from NCI Imaging Data Commons.Left: Magnetic Resonance Imaging (MRI) of the prostate (credit: http://doi.org/10.7937/K9/TCIA.2018.MR1CKGND), along with the annotations of the prostate gland and substructures.Right: highly-multiplexed fluorescence tissue imaging of melanoma (credit: https://humantumoratlas.org/hta7/). The…

SQL

9 min

Demystifying BigQuery BI Engine

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. Using BI Engine is simple – create a memory reservation on the project that runs BigQuery queries, and it will cache data and use the optimizations. This…

6 min

Transforming Customer Experiences With Modern Cloud Database Capabilities

From professional sports leagues to kidney care and digital commerce, Google Cloud databases enable organizations to develop radically transformative experiences for their users. The stories of how Google Cloud Databases have helped Box, Credit Karma, Davita, Forbes, MLB, and PLAID build data-driven applications is truly remarkable – from unifying data lifecycles for intelligent applications, to reducing, and even eliminating operational burden. Here are some of the key stories that customers shared at Google Cloud Next. Box modernizes its NoSQL databases with zero downtime with Bigtable A content cloud, Box enables users to securely create, share, co-edit, and retain their content…

4 min

Break Down Data Silos With The New Cross-Cloud Transfer Feature Of BigQuery Omni

To help customers break down data silos, we launched BigQuery Omni in 2021. Organizations globally are using BigQuery Omni to analyze data across cloud environments. Now, we are excited to launch the next big evolution for multi cloud analytics: cross-cloud analytics. Cross-cloud analytics tools help analysts and data scientists easily, securely, and cost effectively distribute data between clouds to leverage the analytics tools they need. In April 2022, we previewed a SQL supported LOAD statement that allowed AWS/Azure blob data to be brought into BigQuery as a managed table for advanced analysis. We’ve learned a lot in this preview period.…

11 min

A Deep Dive Into Spanner’s Query Optimizer

Spanner is a fully managed, distributed relational database that provides unlimited scale, global consistency, and up to five 9s availability. It was originally built to address Google’s needs to scale out Online Transaction Processing (OLTP) workloads without losing the benefits of strong consistency and familiar SQL that developers rely on. Today, Cloud Spanner is used in financial services, gaming, retail, health care, and other industries to power mission-critical workloads that need to scale without downtime.Like most modern relational databases, Spanner uses a query optimizer to find efficient execution plans for SQL queries. When a developer or a DBA writes a…

4 min

Build Limitless Workloads On BigQuery: New Features Beyond SQL

Our mission at Google Cloud is to help our customers fuel data driven transformations. As a step towards this, BigQuery is removing its limit as a SQL-only interface and providing new developer extensions for workloads that require programming beyond SQL. These flexible programming extensions are all offered without the limitations of running virtual servers.In the keynote of our recent Next 22 conference, we announced a way for customers to incorporate scalable and distributed machine learning and ELT jobs directly into BigQuery’s fully serverless environment with a Preview of Stored Procedures for Apache Spark. We also announced seamless movement from SQL…

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