Systems | Development | Analytics | API | Testing

Choosing one over the other can come down to a few key differences. Let’s take a look at both to help you make an informed decision.

In the article “From Data to Insights: An Introduction to Product Analytics”, we walk you through the basics of product analytics, providing you with a high-level approach to get started with it. This time, we’d like to dig in deeper to dissect every step of the product analytics process, assuming that the main goal is to improve user engagement and retention.

Data engineers who work with huge amounts of data know that “big data” is not just an overhyped term. When the volumes of data get into petabytes the best data engineering tools start to break down. This is when you need devoted big data technologies that are fault-tolerant, scalable, and offer high performance even when amounts of data test the limits of your data platform. This article won’t be just another listicle. Instead, we’ll showcase the best big data tools by use case.

A data extraction tool can help improve the accuracy of data by automating the extraction process and reducing the risk of human error. This can lead to more reliable and consistent data that can be used to make better business decisions. Moreover, data extraction tools can help you increase productivity and improve the quality of your data as they automate the process of retrieving data from multiple sources.

Much like Apple people tend to be all Apple, all the time, Microsoft Dynamics ERP users tend to prefer Microsoft products for all their computing needs. It’s not hard to understand why. Using products from the same ecosystem prevents compatibility issues and saves time in learning multiple systems.

CIOs are fed up with having a plethora of BI and analytics tools with business units seemingly chasing the shiniest new solution. And although most industry surveys show data and analytics budgets continuing to grow despite a faltering economy, there is closer scrutiny and belt tightening to rid teams of overlapping capabilities. Here’s a look at how BI tool portfolios have become such a mess and how to streamline them.

Cloudera SQL Stream builder gives non-technical users the power of a unified stream processing engine so they can integrate, aggregate, query, and analyze both streaming and batch data sources in a single SQL interface. This allows business users to define events of interest for which they need to continuously monitor and respond quickly. A dead letter queue (DLQ) can be used if there are deserialization errors when events are consumed from a Kafka topic.