Data Analytics

Data Warehouse, Data Lake, Data Mart, Data Hub A Definition of Terms

Data Warehouse, Data Lake, Data Mart, Data Hub A Definition of Terms
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.
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Azure Synpase Analytics & Power BI

Azure Synpase Analytics & Power BI
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…
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Announcing support for backup and restore of Power BI datasets

Announcing support for backup and restore of Power BI datasets
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).
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The 7 Tasks in Data Science Management

The 7 Tasks in Data Science Management
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?
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Build a Lake House Architecture on AWS

Build a Lake House Architecture on AWS
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 […]
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How a Data Catalog Enables Data Democratization

How a Data Catalog Enables Data Democratization
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.
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Limitations Of Power Bi

Limitations Of Power Bi
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.…
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15 data science tools to consider using in 2021

15 data science tools to consider using in 2021
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.
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BrandPost: Data & analytics: The next phase of digital transformation

BrandPost: Data & analytics: The next phase of digital transformation
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.
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How to organize your data science project in 2021

How to organize your data science project in 2021
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.
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Why So Many Data Scientists Quit Good Jobs at Great Companies

Why So Many Data Scientists Quit Good Jobs at Great Companies
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.
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