Azure Purview is a unified data governance service that helps you manage and govern your on-premises, multi-cloud, and software-as-a-service (SaaS) data. Gaurav Malhotra joins Scott Hanselman to show how easy it is to create a holistic, up-to-date map of your data landscape with automated data discovery, sensitive data classification, and end-to-end data lineage so that you can empower your data consumers to find valuable, trustworthy data.
[0:01:08]– What is Azure Purview?
[0:02:21]– Azure Purview in the Azure portal and Purview Studio
[0:03:08]– Automated data discovery and scans
[0:10:43]– Purview Data Catalog – Glossary terms
[0:14:08]– Purview Data Catalog – Data classification
[0:15:28]– Purview Data Map – Lineage of assets
[0:19:42]– “Open in” and bulk edit experiences
[0:21:02]– Purview Data Insights
#azure #aft #azure purview
The first step is to understand what is data governance. Data Governance is an overloaded term and means different things to different people. It has been helpful to define Data Governance based on the outcomes it is supposed to deliver. In my case, Data Governance is any task required for:
Compliance, Privacy, and Security are different approaches to ensure that data collectors and processors do not gain unregulated insights. It is hard to ensure that the right data governance framework is in place to meet this goal. An interesting example of an unexpected insight is the sequence of events leading to leakage of taxi cab tipping history of celebrities.
#databases #big-data-and-governance #data-lineage #data-governance #what-is-data-governance #data-governance-explained #data-governance-and-privacy #data-governance-problems
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
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.
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).
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
The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.
Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.
In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.
Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.
Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.
#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations
All the regulations around data compliance and protection such as European Union’s GDPR, California’s CCPA, Australia’s Privacy Amendment (Notifiable Data Breaches) to Australia’s Privacy Act, and other similar regulations, force companies to go all into data governance to meet regulatory requirements. Besides regulatory compliance, companies are aware that they need to have a sound data governance framework if they want to have readily available, relevant and high-quality data for their projects.
When we talked to Nicola Askham, The Data Governance Coach, at last year’s Data 2020 Summit, we discovered that one of the most common challenges that firms come across when drafting a data governance policy is that they don’t know how much data governance is enough.
As Nicola Askham presented in her interview, some companies are too aspirational. They go from having no control over their data to wanting to have everything in place, which is a huge step to take.
While at the same time, there’s the opposite end of the extreme as well. Some people worry that if they make it too much hard work nobody will sign it off. So they include very little in their policy and end up not helping the organisation to mature in terms of their data governance.
#big data & cloud #data governance #data governance framework #data governance policy