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TDWI Upside - Where Data Means Business

Data Lakes and Data Storage

Data Digest: Big Data Pitfalls, Customer Data Integration, Data Retention Tips

Today learn about the blunders many enterprises make with big data, improving your customer data with more integration, and choosing the right amount of data to store.

Data Digest: Securing the Cloud, Understanding Data Lake Pitfalls, Choosing Self-Service BI Tools

Tactics and advice for securing cloud data, problems with data lakes, and myths about self-service BI tools.

Data Digest: Data Manipulation Threats, Successful Data Lakes, Defining BI and Analytics

Find out why the next cyberattack might include data manipulation, best practices to make your data lake successful, and the differences between BI, data analytics, and data science

Data Digest: IoT Data Privacy, IoT Architecture, Data Lake Basics

Today read about privacy concerns that consumers have around IoT data, updating your architecture to take advantage of IoT devices, and the structure, history, and benefits of data lakes.

Data Digest: Uses for Leftover Data Sets, Drawbacks of Data Lakes, Cloud Best Practices

These articles explain why leftover “garbage” data sets actually have value, why you should be skeptical about data lakes, and what to consider when integrating cloud computing to your enterprise.

How Customers Drive BI and Analytics Innovation

The upcoming TDWI Executive Summit in San Diego will explore innovations in BI, analytics, and data management that are essential for achieving customer intelligence and better customer experiences.

Data Digest: Choosing Data Storage, Managing Big Data, BI Basics

In these articles, read the pros and cons of choosing a data lake versus a data warehouse, learn how to manage the growing flood of data, and understand the basic principles and tools of modern BI.

SQL, NoSQL, or Hadoop? Determining the Best Platform for Your Purpose

An increasing variety of data types, analytics needs, and business uses drive data architecture decisions today. Follow this real-world, informal evaluation of SQL, NoSQL, and Hadoop solutions that address a business use case.

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