We now live on the internet! DataReportal states that around 4.80 billion of the global population use the internet, as of July 2021. That’s around 61% of the entire world population. From reading the news and booking tickets to buying groceries and meeting people — everything is now online. In the vast ocean of mobile apps and websites/web apps, we’re going to talk about web apps and web testing in this post.

Traditional Software Development Life Cycle such as Waterfall Model relied on step by step movement from left to right in SDLC. It started from requirement gathering and moved towards the right one step at a time. Evidently, testing came last in the cycle and any delays in it resulted in coinciding with the deadlines. That had the testing team stressed out because the bugs should be identified and fixed before the software release.

The best way to appreciate key concepts involving digital transformation is to look at real-world examples. In a recent Kong webinar, I sat down with Solutions Engineer Ahmed Koshok as he reviewed several real-world case studies that help illuminate the role of microservices in making digital transformation successful for organizations. The case studies included Papa John’s, NextJ Systems, and Yahoo! Japan.

Microservices are an accelerating trend thanks to rousing endorsements from the likes of Google, Netflix, and Amazon. The microservice architecture is advantageous for it’s scalability, agility and flexibility. In contrast, the monolithic approach is the traditional tried-and-true model for building software. It’s much easier to debug and test. But how do you know which approach is best for your organization?

In the previous article we discussed how to enable a server initiated encrypted connection to a Microsoft SQL Server. But what if we have a scenario where we do not want to incur the overhead of encryption for every application? In that scenario instead of configuring the server to force encryption we will instead need the client to initiate the encrypted connection.

Low-code automation platforms enable businesses to rapidly automate their applications and workflows. Basic low-code tools can create applications that help users perform simple tasks. But rapid app delivery is only the beginning of what low-code can do. The most advanced low-code automation platforms deliver powerful business process automation capabilities.

Thanks to its user-friendliness and popularity in the field of data science, Python is one of the best programming languages for ETL. Still, coding an ETL pipeline from scratch isn’t for the faint of heart — you’ll need to handle concerns such as database connections, parallelism, job scheduling, and logging yourself. The good news is that Python makes it easier to deal with these issues by offering dozens of ETL tools and packages.

Data encryption is one of the smartest things any organization can do to protect the privacy and security of confidential and sensitive data. Using a unique encryption key, data is converted to an intermediate representation known as “ciphertext,” which usually appears as a jumbled mixture of letters and numbers to the human eye. This encrypted data will be meaningless to anyone without the corresponding decryption key—even malicious actors who breach an organization’s defenses.

Migros is the largest retailer in Turkey, with more than 2500 outlets selling fresh produce and groceries to millions of people. To maintain high-quality operations, the company depends on fresh, accurate data. And to ensure high data quality, Migros depends on Talend. The sheer volume of data managed by Migros is astonishing. The company’s data warehouse currently holds more than 200 terabytes, and Migros is running more than 7,000 ETL (extract, transform, load) jobs every day.

Every enterprise is trying to collect and analyze data to get better insights into their business. Whether it is consuming log files, sensor metrics, and other unstructured data, most enterprises manage and deliver data to the data lake and leverage various applications like ETL tools, search engines, and databases for analysis. This whole architecture made a lot of sense when there was a consistent and predictable flow of data to process.