In part one of this series, we successfully built a TypeScript Lambda on the AWS cloud. But we left a lot of room for improvement in terms of the developer experience. For starters, the Lambda didn’t run on a local machine, which is cumbersome. The code we wrote is also not testable, which makes refactoring hard or, at least, dangerous. In this take, let’s focus on improving the developer experience. The goal is to make the code more robust and easier to work with. Ready?

It’s no secret that advancements like AI and machine learning (ML) can have a major impact on business operations. In Cloudera’s recent report Limitless: The Positive Power of AI, we found that 87% of business decision makers are achieving success through existing ML programs. Among the top benefits of ML, 59% of decision makers cite time savings, 54% cite cost savings, and 42% believe ML enables employees to focus on innovation as opposed to manual tasks.

APIs (Application Programming Interfaces) facilitate interaction between two programs. For example, an application providing weather details talks to the API created by the weather department to get all relevant information. SlashData found that more than 90% of developers use API- a feat significant to demonstrate the significance of the interface.

You have been putting in the work, and your company has been growing manifold, Your client base is growing more than ever, and the projects are pouring in. So what comes next? it is now time to focus on the data that you are generating. When programming an application, DevOps engineers keep track of many things, such as bugs, fixes, and the overall application performance. This ensures that the application operates with minimum downtime and that any future errors can be predicted.

The key to Extract-Load then Transform (ELT) is that the data is landed in a normalized schema. Why? Correctness, flexibility and understandability.

When you look closer at the Modern Data Stack (MDS) you need to brace yourself. The number of tools companies use for their databases, user administration, data extraction, data integration, security, machine learning, and a myriad of other use cases has grown astronomically. Matt Turck, VC at FirstMark, composes a yearly infographic of the hot tools in the datascape: And this is just a shortlist of both the most popular and fastest-growing tools.

Billions of people rely on WhatsApp each day to communicate in realtime. Friends exchange memes, expats catch up with their families, businesses take bookings and run customer support, and teams ranging from emergency services to on-call engineers at tech companies even use WhatsApp as their primary communication tool. So when WhatsApp had an hours-long global outage on 25 October 2022, the world noticed.

SQL is the universal language of data modeling. While it is not what everyone uses, it is what most analytics engineers use. SQL is found all across the most popular modern data stack tools; ThoughtSpot’s SearchIQ query engine translates natural language query into complex SQL commands on the fly, dbt built the entire premise of their tool around SQL and Jinja. And within your Snowflake data platform, it’s used to query all your tables.

Gaining insights from your data can be time-consuming. Or as simple as a few clicks. Depending on if you want to do everything by yourself, or if you let us help. For example, next time one of your stakeholders asks: “Can you deliver the dashboards by the end of next week?” You can say yes with confidence, and in this blog we are going to show you why and how it is done.

Current data architecture is going through a revolution. Enterprises are starting to shift away from the monolithic data lake towards something less centralized: data mesh. Data mesh is a relatively new concept, first coined in 2019, that addresses potential issues with data warehouses and data lakes that can cause businesses to be slow, unresponsive, or even suffer from data silos. Data mesh benefits are able to provide a wealth of advantages to your business.