Systems | Development | Analytics | API | Testing

Data In Motion: NASA and Aurica

Some 300 million years ago, Earth had one continent called Pangea. Over millions of years, that vast single land mass broke up and drifted in different directions, creating the seven continents that exist today. Since the planet changed so dramatically over millennia, it raises an obvious question: How will it change in the future? The same forces, plate tectonics and continental drift, that broke up Pangea hundreds of millions of years ago still exert themselves.

5 Secrets to Understanding Value Shoppers

Our five key points: Value shoppers are the savvy buyers, the ones always on the lookout for the best deals and who knows where to find every discount and coupon. If you're an e-commerce retailer, the importance of understanding your value shoppers can’t be overstated. Consumers searching for a great deal will absolutely look elsewhere if they’re not impressed by your online offerings.

Modernizing the Analytics Data Pipeline

Enterprises run on a steady flow of best-fit data analytics. Robust processes ensure these assets are always accurate, relevant, and fit for purpose. Increasingly, organizations are implementing these processes within structured development and operationalization “pipelines.” Typically, analytics data pipelines include data engineering functions such as extract-transform-load (ETL) and data science processes such as machine-learning model development.

Commerzbank | Unleashing Hidden Data Treasures for Customers

Like many financial institutions, Commerzbank was challenged with staying flexible to meet customer needs, while also meeting regulatory compliance. In this Movers & Makers, Justyna Lebedyk, Product Owner in Big Data for Commerzbank, talks about how their digital transformation with the hybrid cloud and Cloudera allowed them to overcome this challenge.

Building and Managing the Modern Datastore: The Data Lakehouse

The 'data lakehouse' is quickly becoming popular in the data analytics community. Data lakehouse architecture combines the benefits of a data warehouse and a data lake. It aims to merge the data warehouse’s data structure and management features along with the flexibility and relatively low cost of the data lake. Watch this panel discussion to learn how the data lakehouse can address the limitations of the data lake and data warehouse architecture to deliver significant value for organizations. Explore why the data lakehouse is an ideal option for enterprise data storage initiatives.

MongoDB vs. PostgreSQL: Detailed Comparison of Database Structures

One of the most important parts of the function of any company is a secure database. With phishing attacks, malware, and other threats on the rise, it is essential that you make the right choice in order to keep your data safe and process it effectively. However, it can be extremely difficult to choose among the wide variety of database solutions on the market today. Two commonly-used options are Mongodb and Postgresql. What do you need to know about MongoDB vs. PostgreSQL?

Unstructured Data Now Generally Available in Snowflake, Processing with Snowpark in Public Preview

We’re excited to announce the general availability of the unstructured data management functionality in Snowflake. We launched public preview of this functionality in September 2021, and since then we have seen adoption by customers across industries for a variety of use cases. These use cases include storing and securing call center recordings, securely sharing PDF documents in Snowflake Data Marketplace, storing medical images and extracting data from them, and many more.