Systems | Development | Analytics | API | Testing

This blog post is a summary of my talk at the iOS Singapore Conference 2022 about scaling up iOS automated tests using fastlane and other tools.

Azure Synapse Analytics, still commonly known as Azure Data Warehouse, is Microsoft's cloud data warehouse that processes relational and non-relational data for analytics. As one of the most popular data warehousing tools, Azure lets you generate real-time insights into almost every aspect of your business, from sales to customer service. But how do you get data to Azure in the first place? That's where an Extract, Transform, and Load (ETL) tool proves useful.

Fitness app development is exploding — what health company isn’t trying to create a fitness app for their customers? According to Allied Market Research, the global fitness app market size was valued at $13.78 billion in 2020 and is projected to reach $120.37 billion by 2030. With the COVID-19 pandemic keeping people at home, consumers are looking for new ways to keep exercise interesting. Adaptable brands are rushing to provide the next best service.

It’s a long tradition at Bitrise that we compile the biggest learnings from the previous year in our annual State of App Development blog post. Let’s take a look at what 2021 brought our way, shall we?

According to 451 Research, 96% of enterprises are actively pursuing a hybrid IT strategy. Modern, real-time businesses require accelerated cycles of innovation that are expensive and difficult to maintain with legacy data platforms. Cloud technologies and respective service providers have evolved solutions to address these challenges.

The start of a new year is a perfect time to reflect on what was accomplished and look forward, re-evaluate what we can do better. Change, although difficult at first, can also be very rewarding. That’s why I was excited to see similar sentiments shared at Thoughtspot beyond.2021 to move beyond the traditional dashboards of the past.

The Snowflake Data Cloud is a powerful place to work with data because we have made it easy to do difficult things with data, such as breaking down data silos, safely sharing complex data sets, and querying massive amounts of data. As customers move to the Data Cloud, their needs and timelines vary—our goal is to meet every customer where they are on their Data Cloud journey.

AI models get smarter, more accurate, and therefore more useful over the course of their training on large datasets that have been painstakingly curated, often over a period of years. But in real-world applications, datasets start small. To design a new drug, for instance, researchers start by testing a compound and need to use the power of AI to predict the best possible permutation.