Apache Airflow ETL in Google Cloud
Are you thinking about running Apache Airflow on Google Cloud? That’s a popular choice for running a complex set of tasks, such as Extract, Transform, and Load (ETL) or data analytics pipelines. Apache Airflow uses a Directed Acyclic Graph (DAG) to order and relate multiple…
Mastering Dataflow: 5 In-Depth Guides to Real-World Applications
Building effective real-time data solutions can be challenging, requiring specialized tools and a deep understanding of streaming data. Dataflow offers the power and flexibility to handle a wide range of use cases. And sometimes a little guidance on how to use it can go a long way….
Building a real-time analytics platform using BigQuery and Bigtable
When developing a real-time architecture, there are two fundamental questions that you need to ask yourself in order to make the right technology choice: Freshness – how fast does the data need to be available? Query latency – how fast do you need to be…
New BigQuery capabilities for data and AI governance
Across industries and disciplines, generative AI is transforming the way we work, from sparking new forms of creativity and revolutionizing customer experiences to unlocking hidden insights within complex data. At the same time, this revolution hinges on high-quality, well-governed, and accessible data. Data may be the…
Redefining work with Gemini, from the browser to the device
In today’s fast-paced digital landscape, organizations are constantly seeking ways to enhance productivity, foster collaboration, and drive innovation. Google offers a comprehensive set of products that empower businesses to do just that. Gemini, the AI assistant from Google, is designed to help you get more…
Introducing Customer Engagement Suite with Google AI
Since 2018, when Google Cloud launched Contact Center AI, Google Cloud has helped thousands of organizations deliver better experiences to millions of their customers and employees through AI-powered features. Now, as new generative AI capabilities are demonstrating increasingly larger value for customer service operations, Google…
Customers are putting Gemini to work
It’s been less than six months since Google Cloud Next, and the pace of innovation across industries has been nothing short of extraordinary. Google Cloud are proud of Google AI leadership and differentiation as we continue pushing the technology frontier for Google customers. From launching more…
Chrome Enterprise Improves Management and Productivity Capabilities for Google Workspace users
In today’s hybrid work environments, the browser has evolved into the primary endpoint for accessing corporate applications and data. This presents both opportunities and challenges for IT administrators who need to secure and manage this critical access point. For organizations leveraging Google Workspace to foster…
How to integrate Gemini and Sheets with BigQuery
I often find myself in Google Sheets. Some would say too often. Since I use Gemini for all kinds of things too, integrating Gemini into my Sheets workflow just makes sense. I can boost my productivity in Sheets with capabilities like summarizing sheets and creating formulas. Gemini is now…
Next-gen search and RAG with Vertex AI
Generative AI has fundamentally transformed how the world interacts with information, and the search industry is no exception. The search landscape is changing rapidly, driven by the rise of large language models (LLMs). Whether they’re interacting with their company’s internal data or browsing public websites,…
Experimenting with Gemini 1.5 Pro and vulnerability detection
Unpatched software vulnerabilities can have serious consequences. Google Cloud want developers to reduce the risks they face by focusing on developing code that is secure by design and secure by default. While secure development can be time-consuming, generative AI can be used responsibly to help…
Cut costs and boost efficiency with Dataflow’s new custom source reads
Scaling workloads often comes with a hefty price tag, especially in streaming environments, where latency is heavily scrutinized. So it makes sense Google want Google pipelines to run without bottlenecks — because costs and latency grow with inefficiencies! This is especially true for most modern…