As we talk to people about their data architectures, one question we often get is, “Can I replace my data warehouse (DW) with a data lake”? In theory, you could, but you really don’t want to. Some differences make both suitable for different purposes, but very complementary in your comprehensive data architecture.
The foundation of a data architecture is the enterprise data warehouse—or, at a minimum, function-oriented DWs that rely on the same governance structure and data definitions. Traditional DW implementations follow a set approach: define the requirements, identify (structured) data sources, define the schema, load and format the data, and then distribute the data through presentation layers.
That works well as long as the data fits what you’ve defined. You can use historical transaction or event data in your DW to figure out what happened and why it might have happened. Based on available data, you can form hypotheses and test them against the data in the warehouse to either confirm or refute your suspicions.
The DW is your go-to, your standby, your Old Faithful. It’s the backbone of your daily business—how you keep your hand on the pulse of what’s happening. It has structured, processed, clean data accessed almost immediately, and—when coupled with best-of-breed BI tools—it can democratize BI and put the power of analysis in the hands of even less-than-tech-savvy users. Also, because it’s governed and contains high-quality data, its answers present a single version of the “truth” no matter who’s asking.
Changes in Data Dictate Changes in Technology
Data warehouses have their strong points, but no one needs to tell you that the nature of data has changed significantly over the past decade. The volume of unstructured and streaming data has surpassed that of traditional, structured data. That flood has given rise to a new repository: the data lake. Data lakes allow you to store your data as-is, without having to structure the data, and format it when it’s queried to enable broader and deeper data exploration by data scientists.
Data lakes ingest raw data—in batches or in real-time—and store it in its native format. The data is curated by capturing metadata, but it’s only formatted on read—i.e., when someone wants to retrieve it. They're also cost-effective because much of the technology is open source and the processing at load time is less.
Data lakes also enable a different analytical process. Instead of forming a hypothesis and testing it against the data, you build analysis “sandboxes” where data scientists can use sophisticated modeling techniques to parse the data to spot trends and patterns. In essence, the data scientist uses a model to make an observation or spot a pattern, and then forms a hypothesis. It’s kind of backward to the EDW analysis approach but complementary.
The data lake is the hot new toy you can’t wait to get your hands on, to see how many new and nifty things you can do with it. Data storage is also relatively less expensive, and the data lake can be easily reconfigured. It’s great for data exploration and discovery, enabling you to generate all sorts of new insights previously unavailable with EDWs.
However, data quality is often spotty (which can be remedied with good data governance), and—here’s the big rub—it’s slow in returning answers to queries because of the transformation that must occur before the results are returned. So, there’s your answer as to why you can’t throw everything into the data lake—it’s slow, can be of specious quality, and it’s meant for exploration and discovery, not mundane business analysis.
Exploring the concept of a Data Lakehouse
The answer to striking a balance between the strengths of data lakes and data warehouses lies in the emergence of a Data Lakehouse. A Data Lakehouse combines the flexibility and cost-effectiveness of data lakes with the structured, high-performance querying capabilities of data warehouses. This hybrid approach allows organizations to have the best of both worlds.
Benefits of a Data Lakehouse:
Flexibility and Scalability: A Data Lakehouse provides the flexibility to store data in its raw format while ensuring it can be queried and processed efficiently. This scalability accommodates the ever-growing volume of data.
Unified Data: By integrating structured and unstructured data within a Data Lakehouse, organizations can access a comprehensive view of their data assets, driving deeper insights and informed decision-making.
Cost-Efficiency: A Data Lakehouse leverages open-source technologies and cloud-based infrastructure, reducing costs compared to traditional data warehousing solutions.
Real-Time Analytics: With the ability to process streaming data, a Data Lakehouse enables real-time analytics and quicker response to changing business conditions.
Data Governance: A Data Lakehouse incorporates governance features, addressing data quality and compliance concerns that were previously associated with data lakes.
The answer isn't an exclusive choice between data lakes or data warehouses. Instead, it's about adopting a Data Lakehouse approach, harnessing the strengths of both to create a more versatile, cost-effective, and powerful data architecture that caters to diverse business needs.
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