Relational databases are great at processing transactions, but they’re not designed to run analytics at scale. If you're a data engineer or a data analyst, you may want to continuously replicate your operational data into a data warehouse in real time, so you can make timely, data driven business decisions.

Deciding to adopt an AI-first strategy is the easy part. Figuring out how to implement it takes a little more effort. It requires a clear-eyed vision built around well-defined goals and a realistic execution plan. Being AI-first means setting up your organization for the future. By leveraging data, analytics, and automation, a company can gain a better understanding of where it is and where it needs to go.

At Talend, we hear every day from our customers that healthier data makes it easier to increase revenue, reduce costs, and mitigate risk. But the journey to healthier data isn’t always easy. To support healthy data, organizations must unify data activities across users with different skill sets and levels of technical expertise through intelligence and self-service capabilities.

What is the one thing that all business leaders need to get started with data analytics? The answer is simple. You need to start with the right analytics platform. Selecting the right BI platform is complex, and there are many things that you need to consider. It will help if you consider that an analytic tool should provide all the features a business needs. It should also have a well-established reputation and a track record of delivering reliable and quality market data solutions.

Are you planning to integrate embedded analytics into your business application? You have made the right decision. Embedding analytics directly into your software, rather than using a third-party, separated access application, gives you a wealth of benefits. For example, it helps you to get maximum value from data, brings analytics deeper into standard workflows, and significant competitive advantage. Your software has in-built capabilities others don't.

Whether they know it or not, every company has become a data company. Data is no longer just a transactional byproduct, but a transformative enabler of business decision-making. In just a few years, modern data analytics has gone from being a science project to becoming the backbone of business operations to generate insights, fuel innovation, improve customer satisfaction, and drive revenue growth. But none of that can happen if data applications and pipelines aren’t running well.

BigQuery's Remote Functions (in preview) make it possible to apply custom cloud functions to your warehouse without moving data or managing compute. This flexibility unlocks many use cases including data enrichment. In this post we demonstrate a pattern for combining BigQuery with the Google Maps API to add drive times to datasets containing origin and destination locations. This enrichment pattern is easily adapted for address geocoding or adding Google Map's place descriptions to locations.

Today we are announcing the Preview of BigQuery Remote Functions. Remote Functions are user-defined functions (UDF) that let you extend BigQuery SQL with your own custom code, written and hosted in Cloud Functions, Google Cloud’s scalable pay-as-you-go functions as a service. A remote UDF accepts columns from BigQuery as input, performs actions on that input using a Cloud Function, and returns the result of those actions as a value in the query result.