Harness the power of data with Azure Data and AI

For any organization to succeed in a world of unprecedented uncertainty, a new level of agility is required. Core to that agility is the ability to quickly gain insights from data at any scale, power ultra-low latency applications that deliver personalized experiences, and empower all users in an organization, regardless of size. This requires a computing platform that provides limitless scale, performance, and possibilities for what can be achieved with data.

To help organizations achieve these limitless capabilities, we shared several announcements today at Microsoft Ignite:

Unmatched analytics

Our commitment to customers is to make analytics in Azure the most performant and secure experience it can be. Azure Synapse Analytics is our limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics in a single experience, enabling data professionals to unlock timely insights to increase agility.

To help customers simplify their migration experience to Azure Synapse, we are announcing Azure Synapse Pathway. With a few clicks, customers can scan their source systems and automatically translate their existing scripts into TSQL. What used to take weeks or months can now be accomplished in minutes. Azure Synapse Pathway will support customers migrating from Teradata, Snowflake, Netezza, AWS Redshift, SQL Server, and Google BigQuery—enabling them to get up and running with Azure Synapse faster than ever.

Comprehensive data governance

To enable customers to discover and govern data better than ever before, we introduced Azure Purview, our unified data governance service, in December. Since its debut, customers have used the services to automatically scan, discover, and classify over 14.5 Billion data assets to get a holistic view of their data estates. Customers can now use Azure Purview to scan Azure Synapse workspaces across serverless and dedicated SQL pools. Synapse users can now also break down operational siloes more effectively than ever before, with the ability to natively discover data with a Purview-powered search within their Synapse workspaces.

Starting today, customers can now automatically scan and classify data residing in Amazon AWS S3, as well as data residing in on-premises Oracle DB and SAP ERP instances. This is in addition to Teradata, SQL Server on-premises, Azure data services, and Power BI, which have been supported data sources since Azure Purview’s debut.

#announcements #azure data and ai #ai #azure data

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Harness the power of data with Azure Data and AI

For any organization to succeed in a world of unprecedented uncertainty, a new level of agility is required. Core to that agility is the ability to quickly gain insights from data at any scale, power ultra-low latency applications that deliver personalized experiences, and empower all users in an organization, regardless of size. This requires a computing platform that provides limitless scale, performance, and possibilities for what can be achieved with data.

To help organizations achieve these limitless capabilities, we shared several announcements today at Microsoft Ignite:

Unmatched analytics

Our commitment to customers is to make analytics in Azure the most performant and secure experience it can be. Azure Synapse Analytics is our limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics in a single experience, enabling data professionals to unlock timely insights to increase agility.

To help customers simplify their migration experience to Azure Synapse, we are announcing Azure Synapse Pathway. With a few clicks, customers can scan their source systems and automatically translate their existing scripts into TSQL. What used to take weeks or months can now be accomplished in minutes. Azure Synapse Pathway will support customers migrating from Teradata, Snowflake, Netezza, AWS Redshift, SQL Server, and Google BigQuery—enabling them to get up and running with Azure Synapse faster than ever.

Comprehensive data governance

To enable customers to discover and govern data better than ever before, we introduced Azure Purview, our unified data governance service, in December. Since its debut, customers have used the services to automatically scan, discover, and classify over 14.5 Billion data assets to get a holistic view of their data estates. Customers can now use Azure Purview to scan Azure Synapse workspaces across serverless and dedicated SQL pools. Synapse users can now also break down operational siloes more effectively than ever before, with the ability to natively discover data with a Purview-powered search within their Synapse workspaces.

Starting today, customers can now automatically scan and classify data residing in Amazon AWS S3, as well as data residing in on-premises Oracle DB and SAP ERP instances. This is in addition to Teradata, SQL Server on-premises, Azure data services, and Power BI, which have been supported data sources since Azure Purview’s debut.

#announcements #azure data and ai #ai #azure data

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Leveraging the Power of Big Data and Small Data

Data has become a catchall expression for organizations. The amount of data filling up the organization through regularly exhausting channels is faltering. Last two years more data has been created in all of earlier history. The speed at which businesses are moving today, combined with the sheer volume of data made by the digitized world, requires other ways to derive value from information.

The expressions “Big Data” and “Small Data” have become popular in the last five to ten years. However, it’s not in every case clear about what both of these terms mean or how they assist us with a better understanding of our customers.

Big Data will be data developed in untold manners, for example, through transactions, clicks, radio-frequency identification readers (RFID), financial data, sensors, and an increasing number of IoT connected devices. Small Data, then again, is the data we assemble through primary research. It isn’t simply assembled from qualitative research -in-home ethnographies, online communities, focus groups, etc – yet in addition from quantitative study research. It’s the place where we ask or notice individuals legitimately to reveal their mentalities, inspirations, and values.

#big data #latest news #leveraging the power of big data and small data #big data and small data #small data #big data

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt