What is a Legacy System?
A legacy system is outdated computing software or hardware that is still in use, but its older technology won’t allow it to interact with newer systems.
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CRM Database: What it is and How to Make the Most of Yours
A CRM database is a resource containing all client information collected, governed, transformed, and shared across an organization.
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6 Real-world Wins from Companies Using Business Intelligence
See how 7 real-world companies — from healthcare to finance to retail — are putting modern BI solutions to work for their organizations.
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Data Gravity: What it Means for Your Data
Learn more about data gravity, and why it is a common occurence in many enterprises.
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Big Data and Privacy: What Companies Need to Know to Ensure Trusted Data
Find out why privacy is an increasingly common concern across organizations that use big data to their advantage.
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Business Intelligence vs. Data Mining: Empowering Data-driven Decisions
BI and data mining work best when used in tandem, enabling businesses to make data-driven decisions that improve internal and external performance.
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Overcoming Healthcare’s Data Integration Challenges
Learn about the myriad of challenges healthcare data present, including: compliance concerns, exponentially growing datasets, and lack of data standardization.
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Cloudera Data Ingestion: Processing Data with Hadoop
Learn about how data ingestion works, and its importance to Hadoop.
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Data Warehouse Testing (vs ETL Testing)
Learn what data warehouse testing is, and how it’s essential for decision-making in organizations.
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What is Customer Data Integration (CDI)?
Customer data integration is the process of collecting and organizing customer data in order to easily share information to an entire organization.
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The Value of Data Quality in Healthcare
Data quality allows healthcare organizations to speed up existing processes and build insights that allow for smarter policy decisions.
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8 Benefits of a Cloud Data Warehouse
Find out why it’s no wonder many businesses are moving to cloud data warehouses.
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Best Practices for Managing Data Quality: ETL vs ELT
Data quality is relevant to ETL as well as ELT. Learn what it takes for data quality tools to be effective in both of these data integration methods.
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What is Serverless Architecture?
Serverless architecture promises the possibility of easy business implementations at a predictable cost.
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Microservices vs SOA: What’s the Difference?
Learn about the differences between the two so you can identify which is best for your business.
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What is Data Fabric?
As with any hot new tech term, you might be wondering: “What is data fabric?” and “Why do I need it?”
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Big Data for Supply Chain Management
Let’s take a look at how big data can make a difference in each stage of the supply chain.
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Data Munging: A Process Overview in Python
Learn about data munging: the process of refining raw data into content or formats better-suited for consumption.
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Data governance with Snowflake: 3 things you need to know
If you have considered Snowflake to manage all of your data integration, here are three important things you need to know about ensuring data governance in your cloud data warehouse.
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Big Data in Media and Telco: 6 Applications and Use Cases
Understanding how to apply big data into your media and telecommunications strategy will improve customer engagement and increase revenue
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What is MySQL? Everything You Need to Know
Get to know one of the most recognizable technologies in the modern big data ecosystem.
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Data Engineering: A Guide to the Who, What, and How
Learn how data engineering lays the foundation for real-world data science application.
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The Truth About the Enterprise Data Warehouse (EDW)
Learn how the Enterprise Data Warehouse (EDW) allows companies to harness all business data.
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Big Data and Agriculture: A Complete Guide
Learn the ways in which industry leaders are leaning on big data to assist with the growing demand and recent climate concerns in the agriculture industry.
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Data Science vs. Business Intelligence
Learn how data science and business intelligence differ when it comes to the type of analysis performed, scope, data integration, and skill set.
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