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In 1978, LEGO introduced a brand new line of construction sets branded LEGO Space. Winning toy of the year in Germany and the UK in 1979, LEGO Space helped drive a 50% increase in sales for LEGO that year. LEGO’s strategy for LEGO Space – and every other theme that LEGO has launched since that early success – is to engage customers by providing detailed instructions and clear pictorials, making it easy for builders to get started quickly without limiting their future ability to create just about anything their imagination allows with the individual bricks included in each set. This same guided approach to building can be applied to data models, especially useful for achieving fast, low-risk, and low-cost models suited for purpose in specific vertical industries.The key is implementing a data architecture that supports prebuilt models without restricting the future customization of models and outputs in the process. Learn how in our final chapter, 'Chapter 9: Let's Put it in Context' of our Future-Proof Cloud Data infrastructure e-book. https://hubs.la/Q02NJhd40

While early science fiction shows like Buck Rogers (1939) and The Fly (1950) depicted teleportation technology, it was Star Trek’s transporter room that made real-time living matter transfer a classical sci-fi trope. While we haven’t built technology that enables real-time matter transfer yet, modern science is pursuing concepts like superposition and quantum teleportation to facilitate information transfer across any distance at speeds faster than light. Thanks, Albert Einstein! Back in the data world, we have no need to wait for these future technologies to arrive. Data practitioners today are already using real-time data pipelines to enable a broad set of use cases ranging from website optimization to reactive and predictive fulfillment and delivery routing. How are brands using real-time data streaming in practice today? Let's discuss in 'Chapter 8: Faster is Better' of our Future-Proof Cloud Data infrastructure e-book. https://hubs.ly/Q02NJljC0

Fans of Isaac Asimov’s Foundation Series (the books, not AppleTV!) know that The Imperial Library on the planet Trantor was both the future galactic repository for human knowledge, and the place where scientist protagonist Hari Seldon developed his theories of Psychohistory – the ability to predict the future with advanced probabilistic mathematics. Asimov was a polymath, and his writings were amazingly prescient regarding artificial intelligence and machine learning today. Back here on present-day earth, data scientists face a similar cost/benefit conundrum. How can I develop useful machine learning algorithms on complex data sets and models – without the heavy lift of engineering everything from scratch? Let's discuss in 'Chapter 7: Share, Share Alike' of our Future-Proof Cloud Data infrastructure e-book. https://hubs.ly/Q02NJltN0

In the Marvel Cinematic Universe, mythical Infinity Stones grant their owners great powers. One stone in particular, the Reality Stone grants its holder the power to manipulate matter and defy the laws of physics. Back in the real world, data engineers and analysts and the data consumers they serve face the reality of complex data whizzing from system to system, and complex data modeling work taking place within modern cloud data warehouses like Snowflake, Google BigQuery, AWS, and Databricks. In reality, point-to-point data integration tools lift-and-shift data bundled into messages from one system to another. But extracted, transformed, and loaded (ETL or ELT) data usually lands in disconnected, unjoined tables. Wouldn’t it be great if data teams had access to a Reality Stone to grant them data manipulation superpowers? Learn more about data atomization in 'Chapter 5: Smaller is Better' of our Future-Proof Cloud Data infrastructure e-book. https://hubs.ly/Q02NJgBP0

In the iconic 1999 cyberpunk movie The Matrix, the human rebel protagonists ascribe meaning to the (virtual) world around them by reading and understanding real-time streams of data. The downward flowing green characters are one of the most recognizable visual hooks of the film. As far as we know, we’re not living in The Matrix (or are we?!). Yet modern data engineers and data practitioners now have the ability to decipher data streamed from SaaS APIs and modern cloud data services, interpreting the information in real-time to create “semantic” models of understanding of the data in advance of analytical use cases and downstream data flows. We believe that defining semantic labels and metadata at ingest – as early as possible in the data pipeline – can provide several key benefits for data analytics practitioners and consumers. Read why in 'Chapter 4: Do it Right the First Time' of our Future-Proof Cloud Data infrastructure e-book. https://hubs.ly/Q02NJj8M0

In the science fiction drama Arrival, linguist Dr. Louise Banks discovers that (spoiler alert!) the language shared by visiting aliens is a rosetta stone that unites Earth’s global powers by enabling shared understanding across countries and cultures. This shared language provides a common “semantic” layer for the people of Earth to communicate and understand each other. When we use the word semantic to describe a data model, we are basically talking about a conceptual model of the data, including objects or entities, how they are classified, and their relationships. What’s the value of this semantic understanding, and how does it help businesses thrive using data as a competitive advantage? Let's discuss in 'Chapter 3: Open for Business' of our Future-Proof Cloud Data infrastructure e-book. https://hubs.ly/Q02NJdpk0

In one of the most underrated and better action movies of the last decade, Edge of Tomorrow, Bill Cage (played by Tom Cruise, of course) is forced to replay a segment of his life over and over and over. Though this replay isn’t voluntary, he quickly realizes he can do things differently on each replay, using trial and error to meet his objectives, ultimately saving the human race from being conquered by an alien civilization. Imagine if we could do that for data enablement. Learn how in 'Chapter 2: Let's See that Replay' of our Future-Proof Cloud Data Infrastructure e-book. https://hubs.la/Q02Mryg-0

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 storage and processing, cloud-native solutions supporting streaming ingestion, and convergence of the data warehouse and data lake into the big-data-analytics-ready lakehouse, how can this possibly still be a challenge? Read our thoughts in 'Chapter 1: Your Permanent Record' of our Future-Proof Cloud Data Infrastructure e-book. https://hubs.la/Q02MrxxS0

What is the biggest problem with enterprise generative AI? Learn GenAI's Big Dirty Little Secret and what you can do about it: https://hubs.la/Q02Mrq950

Excited to see our Reactor ? Intelligent Data Pipeline technology highlighted! Way to go Balsam Brands - another happy client activating their data to move their business forward!