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Future-proofing your analytics tech stack is essential for ensuring the longevity and success of your software applications. As the final stage of the data journey, analytics transforms raw data into actionable insights that directly impact business decisions and customer satisfaction. To effectively fulfill this role, analytics systems must possess a high degree of flexibility and scalability, seamlessly integrating with diverse applications and data sources.

It’s a new world—again. Data today isn’t what it was five or ten years ago, because data volume is doubling every two years. So, how could ETL still be the same? In the early ‘90s, we started storing data in warehouses, and ETL was born out of a need to extract data from these warehouses, transform it as needed, and load it to the destination. This worked well enough for a time, and traditional ETL was able to cater to enterprise data needs efficiently.

Choosing the right business intelligence (BI) solution is pivotal for independent software vendors (ISVs) and enterprise organizations aiming for data-driven decision-making. But with so many preferences and use cases to account for, certain considerations can, initially, get overlooked. Centralized business intelligence (BI) is one such topic that requires careful preparation to achieve a nuanced understanding.

Industry leaders sat down to explore the need for company-wide AI readiness, knowledge graphs and using data curation to drive smarter business decisions.

Welcome to the second installment of our blog series to understand the inner workings of the beautiful black box that is Apache Kafka. We’re diving headfirst into Kafka to see how we actually interact with the cluster through producers and consumers. Along the way, we explore the configurations that affect each step of this epic journey and the metrics that we can use to more effectively monitor the process.