Systems | Development | Analytics | API | Testing

Is your small business struggling with inflation? Not sure whether to cut costs or optimize spend? We’ve analyzed 55 small businesses in an attempt to help you solve this dillemma.

What’s harder than finding the right data architecture? Finding the right dedicated partner. Adverity gets both with Snowflake. Learn how the two organizations are moving into new markets and supplying even more reliable marketing data to Adverity customers. When a fast-growing SaaS business looks to expand its client base, it normally encounters two major challenges: In many cases, an external data solution provider can only help solve the scalability challenge.

As organizations invest ever more heavily in modernizing their data stacks, data teams—the people who actually deliver the value of data to the business—are finding it increasingly difficult to manage the performance, cost, and quality of these complex systems. Data teams today find themselves in much the same boat as software teams were 10+ years ago. Software teams have dug themselves out the hole with DevOps best practices and tools—chief among them full-stack observability.

We previously wrote how the data mesh architecture rose as an answer to the problems of the monolithic centralized data model. To recap, in the centralized data models, ETL or ELT data pipelines collect data from various enterprise data sources and ingest it into a single central data lake or data warehouse. Data consumers and business intelligence tools access the data from the central storage to drive insights and inform decision-making.

In today’s competitive environment, organizations need to quickly and easily make decisions based on real-time data. That’s why we’re announcing Datastream for BigQuery, now available in preview, featuring seamless replication from operational database sources such as AlloyDB for PostgreSQL, PostgreSQL, MySQL, and Oracle, directly into BigQuery, Google Cloud’s serverless data warehouse.

More data is available than ever, challenging organizations to change how they interact with their data so they can get the most out of it. Ahmed Munir, Lead SAP Functional Technology Architect Manager at Whirlpool Corporation, has 16 years of SAP leadership experience. He joins us to share what he has learned about building great data teams, upcoming trends in data analytics to keep an eye on, and how data teams will evolve over the next 5-10 years.

Visualizing data is an important aspect of presenting insights clearly. But it's not always easy to create an effective visualization that people will understand on their first glance, or even second. So how do you create the kinds of graphs and tables that leave key stakeholders thinking, " Wow! I need this information!" In this post, we will discuss the top nine best practices for data visualization.

Apache Ozone is a distributed, scalable, and high-performance object store, available with Cloudera Data Platform (CDP), that can scale to billions of objects of varying sizes. It was designed as a native object store to provide extreme scale, performance, and reliability to handle multiple analytics workloads using either S3 API or the traditional Hadoop API.