Data Analytics

In today’s business environment, most organizations are overwhelmed with data and looking for a way to tame the data overload and make it more manageable to help team members gather and analyze data and make the most of the information contained within the walls of the enterprise. When a business enters the domain of data management, it can often get lost in a morass of terms and concepts and find it nearly impossible to sort through the confusion. Without a clear understanding of the various categories and iterations of data management options, the business may make the wrong choice or become so mired in the review process that it will give up its quest.

Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing and big data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources at scale.I quite agree with the bit related to freedom. Synapse has all the tools you need for different workloads or requirements. If you need a plain data warehouse, then use dedicated or serverless SQL pools. If you want Spark, then Spark pools are available with friendly notebook experience like Databricks (downside is that latest Spark versions are not there yet). Data factory is also integrated with Synapse workspace so there is no need to jump here and there among different tools. Source control is also baked into the same recipe if you want…

Data sprawl is the result of trying to serve different workload and priority needs across your business. Here's how to fix it.

We are excited to announce the public preview of Backup and Restore for datasets in Power BI Premium and Premium per User (PPU). You can now use SQL Server Management Studio (SSMS), Analysis Services cmdlets for PowerShell, and other tools to perform backup and restore operations in Power BI via XMLA endpoints in much the same way as you would for tabular models in Azure Analysis Services (Azure AS).

An ever increasing number of use cases for data science are evolving in most companies from nearly every sector. From small businesses to big industries, the number of data scientists is continuously increasing and with them, the size and complexity of data science teams gets bigger. At the same time, it is reported that only a few (22%) data science projects show high revenue and big data projects fail in large numbers (60 to 85%) [Atwal 2020]. This leads to the question: how to deal with the complexity of managing data science teams?

Qlik had the honor to participate in the Gartner Modern Analytics and BI Bake-Off for the 7th year in a row, with involvement reserved for BI vendors based on market interest and attention. It was a Virtual Session, again, this year on May 5th 2021, closing off an exciting day two of the Gartner Data & Analytics Virtual Summit, Americas.

This article was published as a part of the Data Science Blogathon. If you have a basic knowledge of tech, you must have ... The post Data Analytics vs Data Analysis, Are they...

Given the apparent limitations with the Google Search Console traffic data available to the SEO community, the data engineering team at Inseev Interactive developed a simple script that allows you to get the data you need in a flexible format for many great analytical views. Better yet, it’s all available with only a few input variables.

Organizations can gain deeper and richer insights when they bring together all their relevant data of all structures and types and from all sources to analyze. In order to analyze these vast amounts of data, they are taking all their data from various silos and aggregating all of that data in one location, what many […]

For many organizations, data is a business asset that’s owned by the IT department. Based on this ‘data ownership’ model, there’s limited access to data across the organization, and no transparency around what’s available internally.

It's an extraordinary apparatus to use for information investigation and finding significant bits of knowledge. Yet, let us broadly expound and find out about the favorable circumstances and detriments of Power BI so you can have some premise to contrast it and different devices.…

In part 1 of the 4-part blog series, “Unleashing the Business Value of Technology Part 1: Framing the Challenge”, I introduced the 3 stages of “unleashing business value”:  1) Connecting to Value, 2) Envisioning Value and 3) Delivering Value

The increasing volume and complexity of enterprise data, and its central role in decision-making and strategic planning, are driving organizations to invest in the people, processes and technologies they need to make sense of and gain insights from their data assets. That includes a variety of tools commonly used in data science applications.

In this two-part series, I explore the two phases of digital transformation that many organizations are undergoing. In part one, I dig into what organizations have done in the first phase of transformation and why they must think differently as they embark on the second phase. In part two, I describe how organizations should approach the second phase of transformation in order to successfully transform their data and analytics estates – with Spark as the foundation of those changes.

Maintaining proper organization of all your data science projects will increase your productivity, minimize errors, and increase your development efficiency. This tutorial will guide you through a framework on how to keep everything in order on your local machine and in the cloud.

ArticleVideo Book This article was published as a part of the Data Science Blogathon. ” A good first impression can work wonders” – J. ... The post How to make an impressive Data...

Today, organizations are increasingly implementing cloud ETL tools to handle large data sets. With data sets becoming larger by the day, unified ETL tools have become crucial for data integration needs of enterprises.

ArticleVideo Book This article was published as a part of the Data Science Blogathon. This post is to compare the performance of different methods ... The post How to manipulate a 20G CSV file...

The role of the Data Scientist continues to offer many great opportunities as a career. However, the 'sexiest job of the 21st century' has lost some of its appeal because of unrealized expectations and how organizations might leverage this type of work. Having a better understanding of how data science typically plays out in the business world can help you achieve the success you want.

A look at top data science developments worth watching in 2021 Data is a crucial asset for modern businesses. In today’s digital age, any data-related term such as data collection,