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Snowflake demonstrates shift to AI with newest features
After a slow start to building an environment for developing AI applications, the vendor unveils new features that show it is catching up to its peers.
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Managing databases in a hybrid cloud: 10 key considerations
To manage hybrid cloud database environments, consider business and application goals, consistency, configuration management, synchronization, latency, security and stability.
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Evaluate and choose from the top 10 data profiling tools
Any effective data quality process needs data profiling. Evaluate key criteria to select which of the top 10 data profiling tools best fits your needs.
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10 best practices for managing data in microservices
Data architects managing loosely coupled microservices applications need to make the right decisions about databases, data ownership, sharing, consistency and failure recovery.
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Data science and analytics Evaluate
8 benefits of using big data for businesses
Big data is a great resource for driving smart business decisions and changes. Here are eight ways that the use of big data is improving how business gets done.
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Database Management Manage
10 best practices for managing data in microservices
Data architects managing loosely coupled microservices applications need to make the right decisions about databases, data ownership, sharing, consistency and failure recovery.
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Data Warehousing Evaluate
On-premises vs. cloud data warehouses: Pros and cons
Data warehouses increasingly are being deployed in the cloud. But both on-premises and cloud data warehouses have pluses and minuses to consider, as explained here.
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Data Management Strategies Evaluate
Data readiness unlocks the potential of AI
AI models rely on data to function. Before implementing AI, make sure your data can support initiatives by evaluating its quality, accessibility, integration and governance.
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Data Integration Evaluate
Evaluate and choose from the top 10 data profiling tools
Any effective data quality process needs data profiling. Evaluate key criteria to select which of the top 10 data profiling tools best fits your needs.
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Data Governance Get Started
Managing databases in a hybrid cloud: 10 key considerations
To manage hybrid cloud database environments, consider business and application goals, consistency, configuration management, synchronization, latency, security and stability.
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Data readiness unlocks the potential of AI
AI models rely on data to function. Before implementing AI, make sure your data can support initiatives by evaluating its quality, accessibility, integration and governance.
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Managing databases in a hybrid cloud: 10 key considerations
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Evaluate and choose from the top 10 data profiling tools
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Data profiling vs. data mining: Why you need both
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Problem Solve
7 steps to create a data loss prevention policy
Data loss prevention is an ever-changing process of proactive and reactive protection and planning. Read on to learn how to set up a successful DLP policy.
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6 data privacy challenges and how to fix them
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Data management best practices key to generative AI success
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Grow data trust to avoid customer and corporate consequences
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10 best practices for managing data in microservices
Data architects managing loosely coupled microservices applications need to make the right decisions about databases, data ownership, sharing, consistency and failure recovery.
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AI boosts efficiency in data management
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Use these 10 steps to successfully build your data culture
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Data management trends: GenAI, governance and lakehouses
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E-Handbook | November 2021
Chief data officer challenges mount amid calls for more value
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E-Handbook | January 2021
Enterprise data lakes hold the key to actionable insights
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E-Handbook | August 2020
Metamorphosis of Kafka Confluent event streaming technologies
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E-Handbook | June 2020
Data integration technologies unify multiple data stores
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E-Handbook | May 2020
Big data security management embraces governance, privacy
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Data Management/Data Warehousing Basics
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data de-identification
Data de-identification is decoupling or masking data, to prevent certain data elements from being associated with the individual.
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What is data management and why is it important? Full guide
Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization, as explained in this in-depth guide.
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database (DB)
A database is a collection of information that is organized so that it can be easily accessed, managed and updated.
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Data management strategies
Snowflake demonstrates shift to AI with newest features
After a slow start to building an environment for developing AI applications, the vendor unveils new features that show it is catching up to its peers.
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Data management strategies
Informatica adds AI assistant, GenAI development interface
The longtime data management vendor's new capabilities simplify use of its platform, enabling nontechnical users to work with its tools and making experts more efficient.
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Data management strategies
DBT Labs unveils AI assistant, more tools to transform data
The data transformation specialist's latest update includes new governance and metadata management capabilities as well as an AI-powered copilot for developer efficiency.