Travis Maier joins David Blank-Edelman to discuss the Azure Advisor recommendation ‘Enable VM backup to protect your data from corruption and accidental deletion’ and walks through what it means and how to get started with the process of backing up your data.
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
Data Loss Prevention is a set of tools and practices geared towards protecting your data from loss and leak. Even though the name has only the loss part, in actuality, it’s as much about the leak protection as it is about the loss protection. Basically, DLP, as a notion, encompasses all the security practices around protecting your company data.
Every company, even if never vocalized it, has or should have at least some DLP practices in place. You obviously use identity and access management that include authenticating users; you also for sure use some endpoint protection for users’ computers. Maybe (and hopefully) you do beyond that. And this all can be called data loss prevention.
#data-protection #cybersecurity #data-backup #data-security #data-breach #personal-data-security #data #cyber-security
Backing up the data is one of the most important processes for businesses. It requires creating a copy of all your data and storing it.
This data can help in case original data is lost or corrupted. Several companies and organizations use different data backup strategies to keep their data safe and easily recoverable in case of any emergency.
#data-backup #data-security #data-privacy #data-protection #data-structures
Modern analytics teams are hungry for data. They are generating incredible insights that make their organizations smarter and are emphasizing the need for data-driven decision making across the board. However, data comes in many shapes and forms and is often siloed away. What actually makes the work of analytics teams possible is the aggregation of data from a variety of sources into a single location where it is easy to query and transform. And, of course, this data needs to be accurate and up-to-date at all times.
Let’s take an example. Maybe you’re trying to understand how COVID-19 is impacting your churn rates, so you can plan your sales and marketing spends appropriately in 2021. For this, you need to extract and combine data from a few different sources:
#data-analytics #data-science #data-engineering #data #data-warehouse #snowflake #data-connector #machine-learning