Systems | Development | Analytics | API | Testing

We are continuing our blog series about implementing real-time log aggregation with the help of Flink. In the first part of the series we reviewed why it is important to gather and analyze logs from long-running distributed jobs in real-time. We also looked at a fairly simple solution for storing logs in Kafka using configurable appenders only. As a reminder let’s review our pipeline again

A pillar of 3rd-Generation BI, “Democratization of Data,” is the critical first step in maximizing the value of your BI solution. But, this means more than just simply opening up the information floodgates. That is not a formula for success. There are several important things to consider when democratizing data throughout your organization. I’ve outlined five of those key considerations below.

It’s recently been announced that we've made the Gartner Magic Quadrant for the seventh time. What’s most exciting is that this year we're in the visionary quadrant. This means that Yellowfin is now recognized for being an innovator in the BI space. We bring products to market that other people follow and that have something different to offer customers of BI software.

It is that time of year again. The much anticipated 2020 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms has been published, and we are elated and humbled to announce that Yellowfin has been recognized as a “Visionary” in this year’s report. It’s our seventh consecutive year in the quadrant and just like last year, we believe we have made the most significant leap of any vendor in it.

Apache Hadoop Ozone was designed to address the scale limitation of HDFS with respect to small files and the total number of file system objects. On current data center hardware, HDFS has a limit of about 350 million files and 700 million file system objects. Ozone’s architecture addresses these limitations[4]. This article compares the performance of Ozone with HDFS, the de-facto big data file system.

Solving a problem programatically often involves grouping data items together so they can be conveniently operated on or copied as a single unit – the items are collected in a data structure. Many different data structures have been designed over the past decades, some store individual items like phone numbers, others store more complex objects like name/phone number pairs. Each has strengths and weaknesses and is more or less suitable for a specific use case.

Over the years, we’ve worked with a lot of software vendors who have embedded analytics into their product and there’s a range of reasons why they’ve chosen to do that. Some want to modernize existing analytics with a better solution, while others want to engage with more users or extend the use of their application to the C-Suite by delivering something of value to management like reporting.