Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single compute engine. Spark is speeding up data pipeline development, enabling richer predictive analytics, and bringing a new class of applications to market. However: Is Spark alone sufficient for developing converged applications? How can you speed up the development of applications which span across Spark and other frameworks such as Kafka, NoSQL databases, and more?
Most enterprises know that they are constantly under attack and are continuously at risk of losing control of sensitive or confidential information. That’s why it is a commonly-accepted best practice to have an incident response plan in place and to practice it regularly.However, after examining the plans and practice exercises of hundreds of clients, we have observed that many organizations still haven’t fully embraced the hard lessons we all can learn from real-world, high-profile security incidents.
HackerEarth in association with Honeywell is pleased to announce its next webinar on The role of artificial intelligence in software engineering to help you learn from the best programmers and domain experts from all over the world.
Red Gate Software
Starting with a database that’s source controlled using Red Gate’s SQL Source Control, we’ll use the SQL Automation Pack and Team City to set up a simple build process that runs every time you commit a change. You’ll see how this process helps you spot and fix errors quicker, and how you can use it to produce an artefact you can use for reliable, repeatable deployments in the future.