Subscribe to the online version of Database Trends and Applications magazine. DBTA will send occasional notices about new and/or updated DBTA.com content.
Trends and Applications
October is "National Cyber Security Awareness Month," putting the spotlight on the need to keep data safe. In particular, as businesses continue to invest in SaaS-based solutions, they must rethink their risk management strategies to prioritize protecting one of their most important assets: SaaS app data. The transition to cloud and the adoption of SaaS-based applications is not a new phenomenon, but the pandemic clearly accelerated the shift. Notably, cloud spending increased 37% to $29 billion during the first quarter of 2020 alone, despite an expected 8% decline in overall IT spending. With hybrid and remote working models now becoming the norm, this reliance on cloud and SaaS will surely continue as organizations look for scalable and cost-effective ways to provide employees with anytime, anywhere access to information.
Today, a data warehouse is used to do more than just integrating data from multiple sources for better, more accurate analysis and reporting. A data warehouse must also be reliable, traceable, secure, and efficient at the same time. It needs to offer these advantages to differentiate itself, especially in business intelligence. This is where good data warehouse governance becomes very important. There are several enterprise data warehouse best practices and governance tips to keep in mind, along with key principles to implement.
AI, machine learning, and edge computing may be all around us, and these technology endeavors all have one important thing in common: Their success depends on the quality of the data fed into them. Data managers recognize that data quality efforts must be improved to meet these new demands and they are concerned about the quality of the data moving through their enterprises. Eight in 10 organizations' data quality efforts are lagging or problematic. These are among the findings of a new survey of 238 data managers conducted by Unisphere Research, a division of Information Today, Inc., in partnership with Melissa.
Organizations focus a majority of their database migration efforts on a single task: synchronizing data from production to their new target database. The migration goal is to have a perfect copy of the production data in the replacement database so that the cutover will be in as small of a maintenance window as possible. While data migration is a critical step in the overall migration project plan, it shouldn't consume the majority of an organization's resources. There are four key areas to consider when planning a migration from MongoDB to Amazon DocumentDB (with MongoDB compatibility.
What types of platforms are most viable for modern data analytics requirements? These days, there are a wide variety of choices available to enterprises, including data lakes, warehouses, lakehouses, and other options—resident within an on-site data center or accessed via the cloud. The options are boundless. It's a matter of finding the best fit for the business task at hand.
Columns - Database Elaborations
Marvel should have an evil villain named "Null." Nulls have always been trouble in the relational world. Certainly, nulls are used all over the place by virtually everyone. Still, that does not mean that nulls are harmless.
Columns - DBA Corner
You might think the title of this article is somewhat controversial, but you should wait to render judgment until you've read to the end. There are several important shifts impacting data management and database administration that cause manual practices and procedures to be ineffective. Let's examine several of these trends.
Columns - Quest IOUG Database & Technology Insights
Wondering what's new from the Oracle Database development team? William Hardie, vice president of Oracle Database product management, shared the latest information about features and enhancements and Oracle's database strategy with regard to on-premise, in-cloud, and hybrid cloud deployments.
Columns - SQL Server Drill Down
The world of the data professionals and DBAs is swirling with threats and risks, and those dangers are on the rise. You're probably accustomed to using longstanding database features to secure your databases, including roles and permissions, and you're likely familiar with working with your identity management admins to control and create your user authentication scheme.
Columns - Next-Gen Data Management
Modern businesses make decisions based on data that's been carefully collected, extracted, formatted, and analyzed. Data powers competitiveness, brings new products to the marketplace, and improves the customer experience. The use cases are nearly endless, but there's a catch: Working with all that data is becoming increasingly complex.
Columns - Emerging Technologies
If you pay attention to the annual "StackOverflow Developer Survey"—and, as a DBTA reader, you probably should—you might be interested in how developers use and rate the various database platforms. Usage responses are unsurprising; MySQL, SQLite, SQL Server, and PostgreSQL all show up as the most widely used databases. But when you look at the most "loved" databases, the results are actually somewhat surprising—Redis consistently shows up as the most loved database platform by developers