Systems | Development | Analytics | API | Testing

Artificial intelligence (AI) has captured the collective imagination. Most news stories focus on the impact AI will have on the future and the productivity growth we might see. Yet, the rank-and-file at most companies know the challenges of truly integrating AI into their workstreams. Organizations need results now. Solving real business challenges requires combining the power of AI with human workers in a seamless workflow.

Traditional ETL processes are time-consuming and complicated to create and manage, making it difficult for businesses and developers to empower their apps with real-time data analytics capabilities. This is where a headless ETL platform can help. As a new approach to data integration, headless ETL solutions maximize data agility through a single API.

Learn the best practices to follow when implementing a test automation suite.

Self-service analytics, also known as self-service business intelligence (BI), means different things to different people. For some, it might just mean an interactive report which lets them drill up and down a hierarchy or maybe switch to a different metric. For others, it might mean complete freedom to access anything in the company’s data warehouse in order to generate strategic insight.

With the world revolutionizing at a fast pace, digital wallets have become the new banking norm, allowing customers to do things on the go. Online transactions have already become an integral part of our life, whether you agree or not. As digitization sped up, the switch to cashless transactions increased enormously. Of course, there are many options for making payments online, including digital wallets, bitcoin, online banks, and credit cards.

Every organization aiming to bring AI to the center of their business and processes strives to shorten machine learning development cycles. Even data science teams with robust MLOps practices struggle with an ecosystem that is in a constant state of change and infrastructure that is itself evolving. Of course, no single MLOps stack works for every use case or team, and the scope of individual tools and platforms vary greatly.

Features like prebuilt data models, version control and integrated scheduling make data transformation faster and more powerful.

Microsoft SQL Server is kind of a swiss army knife for most SME needs and workloads. However there are a handful of things that SQL Server will be better at, and there’s a handful of things Snowflake will be better at.

Often businesses have a different Database to store transactions (Eg: Amazon Aurora) and another Data Warehouse (Eg. Snowflake) for the company’s Analytical needs. There are 2 prime reasons to move data from your transactional Database to a Warehouse (Eg: Aurora to Snowflake).