Systems | Development | Analytics | API | Testing

IT leaders pick different clouds for many reasons, but the rest of the company shouldn’t be left to navigate the complexity of those decisions. For data analysts, that complexity is most immediately felt when navigating between data silos. Google Cloud has invested deeply in helping customers break down these barriers inherent in a disparate data stack. Back in October 2021, we launched BigQuery Omni to help data analysts access and query data across the barriers of multi cloud environments.

Protecting sensitive data and preventing unintended data exposure is critical for businesses. However, many organizations lack the tools to stay on top of where sensitive data resides across their enterprise. It’s particularly concerning when sensitive data shows up in unexpected places – for example, in logs that services generate, when customers inadvertently send it in a customer support chat, or when managing unstructured analytical workloads.

The cloud is growing more and more popular each day. We are in an era where there is a prominent trend of companies migrating from traditional on-premise systems to more reliable and fast cloud-based systems. However, the conversion is still not rampant on a large scale, primarily due to the lack of awareness in the up-and-coming businesses about the cloud’s fundamentals. However, the cloud has proven to be a sound and worthy option time and time again.

Your company needs data replication for analytics, better performance and disaster recovery.

The cloud data platform combines data warehouse and data lake capabilities to support the exploding world of analytics. Like a data warehouse, the cloud data platform structures, transforms, and queries data. Like a data lake, it classifies multi-structured data objects in an elastic object store. The cloud data platform provides an ideal launchpad for modern business intelligence (BI) projects that need fast, flexible access to lots of varied data. As you might expect, this is a tall order to fill.

Major technology events are back, and so are we. The Atlassian Team ‘22 event in Las Vegas happened last week and we were thrilled to attend in person. And usually, what happens in Vegas stays in Vegas. But today, we’re talking all about what took place, who was there, and how we participated. So, what were the most memorable moments at Atlassian Team ‘22? Read on to find out.

The five main reasons to implement a fully automated data pipeline are: When you think about the core technologies that give companies a competitive edge, a fully automated data pipeline may not be the first thing that leaps to mind. But to unlock the full power of your data universe and turn it into business intelligence and real-time insights, you need to gain full control and visibility over your data at all its sources and destinations.

If you’re looking to buy software for your business, you might struggle to find a solution that does exactly what you need it to do. The more complex the need — application lifecycle management, for example — the less exact the fit. Your final decision will probably depend on which makes more sense for you: customization or configuration. A highly configurable tool may cost more on the front end, but your total cost isn’t just the price tag.

We’re excited to announce the general availability of the unstructured data management functionality in Snowflake. We launched public preview of this functionality in September 2021, and since then we have seen adoption by customers across industries for a variety of use cases. These use cases include storing and securing call center recordings, securely sharing PDF documents in Snowflake Data Marketplace, storing medical images and extracting data from them, and many more.