Sidekiq is one of the most popular open-source background job libraries for Ruby. As one of ActiveJob's most popular backends, it's often used to run asynchronous jobs in Rails applications. It leans on Redis to manage queues and jobs, which makes it fast. Developers can run background jobs using Sidekiq with or without ActiveJob, and we'll explore both in this article. Just as useful - Sidekiq scheduled jobs allow you to run a job after a given amount of time or at a given time.

Have you noticed how quickly the retail industry is changing? It's all thanks to digital transformation, which is happening at an incredible pace. We bet you're curious about why this shift is happening and what's driving it. Don't worry, you're not alone in your questions. Many people are wondering about the same thing. With each passing day, we see new technologies and IT solutions being adopted by retailers to stay ahead of the competition and offer better user experiences.

Check Point Research found that global weekly attacks rose by 7% in Q1 2023 versus the same quarter last year, with each organization facing an average of 1248 attacks per week. In 2023 researchers noticed that it’s taking organizations an average of 49 days to identify a cyberattack. That’s terrifying, isn’t it? But, what does this mean? Is this a signal that your business needs a foolproof web development strategy to prevent your website from cyberattacks?

A SQL injection attack is an attempt to modify the syntax of a SQL query executed in conjunction with a user-initiated action within a software application. Some examples of user-initiated actions include searching for a product within an e-commerce catalog, adding a comment to a blog post, and creating a new user account. In each of these scenarios, the user uses a form to submit input (e.g.

Gen AI has the potential to bring immense value for marketing use cases, from content creation to hyper-personalization to product insights, and many more. But if you’re struggling to scale and operationalize gen AI, you’re not alone. That’s where most enterprises struggle. To date, many companies are still in the excitement and exploitation phase of gen AI. Few have a number of initial pilots deployed and even fewer have simultaneous pilots and are building differentiating use cases.

Every enterprise is modernizing their business systems and applications to respond to real-time data. Within the next few years, we predict that most of an enterprise's data products will be built using a streaming fabric – a rich tapestry of real-time data, abstracted from the infrastructure it runs on. This streaming fabric spans not just one Apache Kafka cluster, but dozens, hundreds, maybe even thousands of them.

Picture this: It's Black Friday, and you're circling a packed mall parking lot. Every space is taken, and cars are lined up waiting for spots. You keep circling, but there’s just no place to park and you run out of gas. When you see a java.lang.OutOfMemoryError it’s just like what you experienced in that overcrowded parking lot. The Java Virtual Machine (JVM) has run out of space to "park" new objects in memory. Now here's the thing about Java: it loves objects. It can't get enough of them.

Understanding embedded analytics dashboards starts with knowing what the term itself means—so let’s break it down. Dashboards are screens or pages that display information in a unified view that makes data easily digestible for end users.

To enhance security and ease operational burden, many organizations with data lakes or lakehouses want flexibility to securely integrate their tools of choice on a single copy of data. An open standard for storage format and catalog API has helped, but there’s still a need for open standards for the catalog, including a consistent way to apply security access controls to data.

More often in today’s society of growing ‘entrepreneurs’, almost all are developing Minimum Viable Products MVPs that breathe life into their concepts. A shocking but true statistic reveals that 90% of startups fail. One big reason for this is the poor execution of a key tool in the startup world: the minimum viable product (MVP). One of the reasons for this failure is the lack of tests conducted appropriately.