Updated June 2023

If your business doesn't have a centralized data repository driving fresh, actionable, well-defined, and governed data, you're falling behind. Quickly. Because keeping up with ever-evolving technology and customer expectations is no easy feat. And sooner than you'd probably expect, your chances of catching up will evaporate like a puddle in Death Valley.

Pretty bold statement, right? Unfortunately, it's not only bold but true as well. Remember, it's not like your competitors are going to hit pause on their march toward data and decision-making superiority so you can catch your breath. Same goes with regulators, data privacy & sharing laws, and your stakeholders. That's just not how it works.

Thankfully, all hope is not lost, assuming you're ready, willing, and able to lasso your data-driven potential. And whether it's already a familiar term, something you vaguely recall hearing at some point, or akin to an alien language, the data lakehouse is going to be a central figure in your company's viability, starting today.

So let's take a closer look at this data storage wunderkind, where it fits into an ecosystem already filled with data warehouses, marts, lakes, and more, and see how a data lakehouse can transform operations across your finance organization and, ultimately, your entire enterprise.

Differences Between a Data Warehouse, Lake, and Lakehouse

Before diving head-first into the data lakehouse concept, it's probably best to first discuss its predecessors in the data warehouse and data lake.

What Is a Data Warehouse?

First came the traditional data warehouse, a revolutionary storage architecture for structured data that entered the scene in the 1980s, exploding in popularity throughout the 1990s along with flannel shirts and Pokemon.

It was a simpler time back then, including what companies needed from data stores. This was pre-Big Data, when organizations weren't worried about retaining reams of video, audio, or text files since the fixed fields and columns in spreadsheets and relational databases were basically all they needed to compute and compete. Further, each was already accessible through structured query language (SQL) that had been around for over a decade by that point.

In other words, businesses needed data management solutions for the mountains of structured data – those spreadsheets and databases – piling up to their knees, and traditional data warehousing fit the bill just fine. However, times change and, as you know, technology and data science stop for no one. Therefore, as the digital world evolved, so did corporate demands of storage solutions and data architectures in general, ultimately prompting the need for an approach besides the data warehouse and the structured data it served so well.

What is a Data Lake?

That's where the data lake comes into play. Companies eventually discovered the veritable goldmine of raw data sitting in unkempt, unwieldy rich media files like video, pictures, and text – later joined by IoT sensor data, stacks of emails, and machine learning applications, to name a few. They also realized data warehouses weren't a good fit for that particular treasure trove of insights.

Put another way, the world needed to store data in an architecture that was well-suited for Big Data and the unstructured data files feeding it, propelling the data lake into the spotlight. With a data lake, you can store a massive amount of data in raw formats, suddenly making use cases like surveillance footage, the past ten years of customer emails, board meeting recordings, and an avalanche of Zoom video conferences mineable.

Just as importantly, thanks to file system and object storage providers like Amazon AWS/Redshift, Microsoft Azure, Snowflake, and other cloud data warehouse solutions, those massive volumes of information can be cost-effective, depending on your needs. Thus, such cloud-based, low-cost storage providers still help propel faster, bigger, badder data pipelines and data processing without driving a meaty stake through your income statement and cash flow's collective heart.

However, since nothing is perfect in this world, organizations now understand data lakes have their drawbacks, primarily around the outright messiness of the files. Yes, it's great to be able to store tons of video in an unaltered data format at a lower cost, but cost isn’t the only factor involved. Because data lakes often look like they just survived a frat party which, suffice it to say, doesn't lend itself to real-time data and concise, revealing business intelligence.

So what is a company to do when it needs to extract, transform, and load (ELT) data sets and other quantitative information into a warehouse, while also reaping the qualitative touch – via extracting, transforming, and loading (ETL) – of a data lake? And all while consolidating its business information into the least number of data sources possible without sacrificing high performance and functionality? Glad you asked.

The Data Lakehouse: The Best of Both Worlds

The data lakehouse is a hybrid, open data management architecture that’s a middle ground between warehouses and lakes, brought to you by Databricks, which happens to come from the very same brain trust as Apache Spark. With a data lakehouse, you can grab data at your own pace and format, from batches to real-time.

Just as importantly, it also adjusts itself to changes in data structures, all while keeping track of different versions of your data. These capabilities ensure your business information stays in lock-step with the shape-shifting, ever-evolving data flows coming in, quickly making it available, usable, and informative for the end users.
 

In fact, a data lakehouse uses a similar data structure and schema to that of a data warehouse, only applying it to both structured and unstructured enterprise data. Thus, you can efficiently categorize and utilize your data, no matter the type and format, for financial reporting or virtually any other use, all without the query engine difficulties and delays that jam-packed data lakes carry along with them.

But the information you feed into a data lakehouse isn't limited to your standard systems and data platforms, of course. You can also use it to store everything from point-of-sale transaction scans and IoT data to insights culled from your company's social media feeds. In other words, the sky's the limit when it comes to the types of data you can load into it.

Also, unlike data lakes, where the information becomes immutable as soon as it touches down, a data lakehouse still allows you to append a file. So, if you load a file and receive an updated version just five minutes later, you can append the most recent version with new data while, once again, still preserving previous iterations. Thus, you avoid the version control apocalypse while retaining the ability to go back to specific points in time.

How Does a Data Lakehouse Work?

Besides the flexibility they provide over their warehouse and lake brethren, a data lakehouse also allows you to provide different user experiences for different needs and personas. For example, a data scientist doesn't want you to filter the data through any nice and friendly filters. Like ODB, they want the information raw.

Conversely, a typical CFO doesn't want a tangled bird's nest of information and file formats. They want it defined and refined, with data management features that let them view business information through data dashboards and drive advanced data analytics. This way, you're getting clean, accurate insights – without errors, redundancies, and duplication – leadership can explore from a laptop, smartphone, or basically any device with an internet connection.

To account for these varying needs and expectations, your data engineers build different storage layers into the data lakehouse. For instance, they can build and designate bronze, silver, and gold levels for your different users. In this example, data scientists prefer the unabridged version of your data in the bronze level, while your C-suite opts for the neat and tidy, easily-navigated data in the gold. 

In the meantime, a data lakehouse also drives data integrity by removing data errors on the fly, a capability that a data lake can't match. Or, put another way, lakehouse architecture fixes broken records rather than forcing you to choose between letting the errors through or rejecting an entire block of records you, a business partner, or even customers might request.

Now, for the sake of brevity, we won't take a deep dive into the intricacies and optimization choices in a data lakehouse. However, we’d be remiss if we didn’t at least touch on the subject. For starters, you can customize the lakehouse experience through open-source tools like Delta Lake and Apache Spark along with various APIs, all helping you shape the data lakehouse to best fit your organization's workloads and specific needs without forgoing an ounce of data quality.

Combine these tools with the headlining native abilities with lakehouses – support for ACID transactions, indexing, schema enforcement, and the aforementioned data validation and version history features – and you have a pretty potent data management foundation to work from.

Data Lakehouse Benefits for a Finance Organization

We could continue expanding on the many benefits a data lakehouse brings to financial organizations and their enterprises. However, we also don't want to wear out our welcome, so let's downshift into bonus round mode and end with a few quick-fire ways a data lakehouse will add tremendous value to your operations:

Data Governance: A data lakehouse is a single source of truth, rather than having your data spread out across multiple repositories. Obviously, this makes data governance a lot simpler, practical, and effective, all traits your auditors will applaud you for

Scalability: With a data lakehouse, you can quickly scale data and metadata as needed, whether to simply keep pace with growth or for larger projects. Therefore, you'll never feel like you're running second or third in the time-to-insight food chain, even if you're undertaking a massive transformation initiative.

Data Security: Since your IT team won't have to actively manage every version of every file that pulls into the data lakehouse, data security is faster and easier. Also, lakehouse architecture lets you maintain precise access controls and encryption, both vital for SOX compliance.

Advanced Analytics: You're going to need fast, in-depth, accurate data to climb the business intelligence curve and leverage the predictive and prescriptive powers of advanced analytics, machine learning, and artificial intelligence. A data lakehouse shines in this regard, providing analytical superpowers that more traditional architecture can't come close to matching.

The bottom line is this – your reliance on structured and unstructured data won't be falling anytime soon. And by anytime, we mean never. In fact, if you want to keep up with a dynamic marketplace and aggressive, motivated competitors, a data-driven strategy for operations and growth is by far the sharpest tool in your arsenal.

That said, if you haven't already run into the pressing need for faster, more reliable, nuanced insights to drive genuine business intelligence, then you will any day now. Thus, whatever your preferred idiom might be – to the victor go the spoils, the early bird gets the worm, no one remembers second place – early adopters to the data lakehouse-fueled future will have a massive competitive advantage. And Embark's Business Transformation team wants to help you claim that top spot. So let's talk.