A data warehouse stores data from in-house systems and various outside sources. Data warehouses are designed to support the decision-making process through data collection, consolidation, analytics, and research. They can be used in analyzing a specific subject area, such as “sales,” and are an important part of modern business intelligence. The architecture for data warehouses was […]
High-Fidelity, Persistent Data Storage and Replay
In arguably the most iconic scene from Bladerunner, replicant Roy Batty describes his personal memories as “lost in time, like tears in rain.” Until immortality is invented, we’ll have to settle for solving the same problem in data enablement. Actionable data lost to time. How are we still talking about this? With incredible advances in data […]
How to Leverage Machine Learning to Identify Data Errors in a Data Lake
A data lake becomes a data swamp in the absence of comprehensive data quality validation and does not offer a clear link to value creation. Organizations are rapidly adopting the cloud data lake as the data lake of choice, and the need for validating data in real time has become critical. Accurate, consistent, and reliable […]
Evaluating Data Lakes vs. Data Warehouses
While data lakes and data warehouses are both important Data Management tools, they serve very different purposes. If you’re trying to determine whether you need a data lake, a data warehouse, or possibly even both, you’ll want to understand the functionality of each tool and their differences. This article will highlight the differences between each and how […]
How to Architect Data Quality on Snowflake
Without effective and comprehensive validation, a data warehouse becomes a data swamp. With the accelerating adoption of Snowflake as the cloud data warehouse of choice, the need for autonomously validating data has become critical. While existing Data Quality solutions provide the ability to validate Snowflake data, these solutions rely on a rule-based approach that is […]
The Dangers of a Data Swamp (and How to Avoid Them)
“Data is the currency of the future,” many experts have predicted. The 21st century has been characterized by the astounding amount of data we’ve gained access to. But what happens if this data isn’t properly stored? A data swamp begins to develop, and accessing that data becomes difficult and sometimes impossible. The internet, social media, […]
The Connection Between Good Data Management and Enterprise Agility
Click to learn more about author Russ Ernst. The word “agile” is defined in one dictionary as “quick and well-coordinated in movement; lithe.” During the last two years, however, the word has taken on an entirely different meaning for companies navigating the complexities of a pandemic. As the realities of a highly contagious virus set in and hundreds of thousands […]
A Brief History of Data Lakes
Data Lakes are consolidated, centralized storage areas for raw, unstructured, semi-structured, and structured data, taken from multiple sources and lacking a predefined schema. Data Lakes have been created to save data that “may have value.” The value of data and the insights that can be gained from it are unknowns and can vary with the […]
Is it Time to Drain the Data Lake?
Click to learn more about author Karthik Ramasamy. It sounds appealing – easily store all of your data in a single location, where all of your users and applications can access it and put it to use. It’s no wonder that interest in data lakes rose rapidly at the same time hype around “Big Data” was […]
Data-as-a-Service: Helping Companies Get More Value From Their Data, Faster
Click to learn more about author Kelly Stirman. Roger Magoulas introduced us to the term Big Data back in 2005. Never did we imagine that data was going to increase in volume so fast that the term itself would become almost irrelevant and big data would become just ‘data’. Big Data also brought with itself concepts […]