Production debugging in its current form is a relatively new area of technology that aims to make it easier for developers to solve problems in their code. More often than not, we don’t have all the information we need to solve those hard to reproduce bugs. This leads to long hours of debugging, adding more log lines, and creating separate reproduction environments to try to isolate and reproduce problems.

We’d just want to let you know that your builds running on Linux are now faster. And that this is included in your plan, so you don’t have to do anything, just enjoy the ride or read the details.

Although analytics and reporting may sound the same, they aren’t. Dive into the difference between the two to learn which one you need.

Earlier this month the Shopware organized their Community Day (SCD) and I presented a talk on Shopware 6 Best Practices for Production. I have now published the slides to this talk for your reference.

Joining us is Alissa Knight, partner at Knight Ink Media, CISO, cybersecurity expert, cinematographer and accomplished content creator for telling brand stories at scale.

BigQuery is a fully-managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and intelligent caching for business intelligence. To help you make the most of BigQuery, we’re offering the following no cost, on-demand training opportunities.

Apache Hive and Apache HBase are incredible tools for Big Data. While there is some overlap in their functions, both Apache Hive and Apache HBase have unique qualities that make them better suited to specific tasks. Some key differences include: Ultimately, comparing Apache Hive to Apache HBase is like comparing apples to oranges or Google to Facebook. While the two entities are similar, they don't provide users with the same functionality.

One of the most substantial big data workloads over the past fifteen years has been in the domain of telecom network analytics. Where does it stand today? What are its current challenges and opportunities? In a sense, there have been three phases of network analytics: the first was an appliance based monitoring phase; the second was an open-source expansion phase; and the third – that we are in right now – is a hybrid-data-cloud and governance phase. Let’s examine how we got here.

In our modern digital society, data is abundant, and storage is affordable. Businesses, governments and even individuals can (and do) collect every transaction, click, swipe, location, message and attribute in their datasets. With just a few clicks on my smart device, I can review data on every place I’ve been, how much I spent, every step I took, what the weather was like and who I was with. Businesses collect the same abundance of data.