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Microsoft Power BI is a business analytics solution providing interactive visualizations and business intelligence capabilities from data and provides an interface that is simple enough for admins to create their own reports and dashboards. Data inputs to Power BI can come from multiple sources – Excel worksheets, CSV files, database tables, log files, the web, etc. It then employs smart visualizations and built-in AI technologies on that data to turn it into interactive insights.

Hadoop ecosystem now serves as a comfortable home to Big Data now, and the Hadoop data stores now have a greater acceptance across the world by programmers, developers, data scientists, and database management experts. These ecosystems are as convenient as the data storages; however, the inherent reporting system of Hadoop poses a few challenges for the users to overcome.

A few blogs posts ago I wrote about new BI for digital companies and in that blog I alluded that quite a bit of that BI is based on log data. I wanted to follow up on the topic of logs, why they exist and why they contain so much data that is relevant to BI. As I said in that post, logs are an artifact of software development and they are not premeditated, they are generated by developers almost exclusively for the purpose of debugging pre-production code. So how is it that logs are so valuable for BI?

In this blog series, we will cover how Amazon Redshift and Sumo Logic deliver best-in-class data storage, processing, analytics, and monitoring. In this first post, we will discuss how Amazon Redshift works and why it is the fastest growing cloud data warehouse in the market, used by over 15,000 customers around the world. When an organization gains traction, the size of data that needs to be stored, monitored, and analyzed expands exponentially.

Hive and Spark are two very popular and successful products for processing large-scale data sets. In other words, they do big data analytics. This article focuses on describing the history and various features of both products. A comparison of their capabilities will illustrate the various complex data processing problems these two products can address.

Starting this week, we will do a series of four blogposts on the intersection of Spark with Kubernetes. The first blog post will delve into the reasons why both platforms should be integrated. The second will deep-dive into Spark/K8s integration. The third will discuss usecases for Serverless and Big Data Analytics. The last post will round off with insights on best practices.

Apache Flume helps organizations stream large log files from various sources to distributed data storage like Hadoop HDFS. This article focuses on the features and capabilities of Apache Flume and how it can help applications efficiently process data for reporting and analytical purposes.

Reporting software is a part of a Business Intelligence or BI suite and is used for analysis in early data processing. The purpose of self-service reporting software is to help deliver interactive information that can be put into action. Self-service reporting software allows the user to connect data sources, extract data and present it in various formats of visualization, including charts, tables, and spreadsheets.

They say knowledge is power and today, there is no greater source of knowledge than data. It is well documented that businesses produce a vast amount of data whether it be in manufacturing, IT, UX, Marketing, Sales or Finance. Collectively, all of this data can provide you with business intelligence, enabling you to optimize decisions, improve collaboration, and increase overall performance.