Are you confused about the multiple types of business process automation? You’re not alone. Automation tool vendors seem to have created more flavors than a contestant on Top Chef. Let’s get to the bottom of the differences between the various types of process automation and find out what they can and can’t do, in plain terms. Here’s a starting hint: Get ready for synonyms. In some cases, different analyst firms and tool vendors use different terms for the same ideas.

The ability to rapidly assemble and reassemble APIs and microservices has become a competitive differentiator across all industries — allowing organizations to pivot business models and deliver amazing customer experiences at speed and scale. Innovative organizations are pushing to become agile and transforming by adopting cloud native technologies that allow them to build more resilient software quickly that easily scales.

Traditional forms of enterprise project management are constantly evolving as the field matures and expands. Although these ways of thinking have been successful in the past, changes to the ways we work, lightning-fast globalization, and the increased use of Agile across industries require a new way of working. As a result, structuring an Agile Project Management Office (PMO) is the next logical step. But what is an Agile PMO? Agile PMO is a modern take on Project Management techniques.

Codes are the core of any application program. These codes can make or break a program. If you are a developer you might have come across certain fault lines while executing a program. Yes, I meant the errors and exceptions. In Java, errors and exceptions are both types of throwable objects that can be thrown by the Java Virtual Machine (JVM) or by Java code.

Elasticsearch is a JSON-based database leaning heavily towards the unstructured types within the databases available out there. ( Postgres and MySQL are purely structured, while NoSQL is entirely unstructured). It interacts through restful APIs and provides a central unit system combining several datasets arising out of logs, metrics, and application trace data. A quick comparison with relational database management systems (RDBMS) will tell us their similarities.

If you were to wake me up at 3 am and ask me how Rookout differs from logs (or other pillars of Observability, for that matter), my answer would have focused on the agile nature of live debugging. I would have explained that Non-Breaking Breakpoints empower you to decide in real-time what data you need. Traditionally, you would use whatever logs (or metrics and spans) happen to be in the code and work backward from there.

Data integration is the process of combining data from different sources and formats to create a unified and consistent view of the data. This involves merging data from multiple databases, applications, and other sources into a single repository, and transforming and formatting data so that it can be easily accessed and analyzed. Data assets need quality controls to ensure they are valid and reliable as many teams within an organization leverages the same data for different purposes.

When it comes to building an API, Rails is an extremely popular programming language choice to build powerful RESTful APIs. In this tutorial, we will build a simple REST API using Rails. The Rails REST API endpoint will allow users to retrieve a credit score rating. Of course, we won’t be linking up to any backend systems to pull a credit score but will instead use a random number generator to generate the score and return it to the user.

In PHP, an enum is a new data type that was introduced in version 8.1. An enum, short for "enumeration," is a set of named constants that represent distinct values that a variable of that enum type can take. Enums are especially useful when you have a set of values that are related and need to be grouped together under a single type. To define an enum in PHP, you use the enum keyword, followed by the name of the enum, and then a list of constant names, each separated by a comma.

With increased applications developed by different engineering teams on Kafka comes increased need for data governance. JSON is often used when streaming projects bootstrap but this quickly becomes a problem as your applications iterate, changing the data structures with add new fields, removing old and even changing data formats. It makes your applications brittle, chaos ensues as downstream consumers fall over due to miss data and SREs curse you.