Key Differences between Data Lake and Data Warehouse
The adoption of cloud computing and shift into big data scope has drastically changed business frameworks. With more data to process and integrate into different workflows, it has become apparent that there is a need for a specialized environment – i.e., data lake and data warehouse.
However, despite its widespread use, there is a lot of confusion regarding the differences between the two (especially in terms of their role in the business workflow). Both are viable options for specific cases, and it is crucial to understand which is good for what.
In this article, we will:
- Explain the differences between lake and warehouse types of architecture.
- Explain in what operations data lakes and data warehouses fit best?
- Show the most viable use cases for data lakes and data warehouses.
A data lake is a type of storage structure in which data is stored “as it is,” i.e., in its natural format (also known as raw data).
The data lake concept comes from the abstract, free-flowing, yet homogenous state of information structure. It is lots and lots of data (structured, semi-structured, and unstructured) grouped in one place (in a way, it is a big data lake).
The types of data present on the data lake include the following:
- Operational data (all sorts of analytics and salesmarketing reports);
- Various backup copies of business assets;
- Multiple forms of transformed data (for example, trend predictions, price estimations, market research, and so on);
- Data visualizations;
- Machine learning datasets and other assets required for model training.
In essence, the data lake provides an infrastructure for further data processing operations.
- It stores all data the business pipeline needs for proper functioning. In a way, it is very similar to a highway – it enables getting the job done fast.
The main feature of the data lake is flexibility.
- It serves the goal of making business workflow-related data instantly available for any required operation.
- Due to its free-form structure, it can easily adjust to any emerging requirements.
- Here’s how it works: each piece of data is tagged with a set of extended metadata identifiers. This approach enables the swift and smooth search of relevant data in the databases for further use.
- Because of its raw state and consolidated storage, this data is open to repurposing for any required operations without additional preparations or transformations at a moment’s notice.
This approach is often applied by companies that gather various types of data (for example, user-related data, market data, embedded analytics, etc.) for numerous different purposes.
- For example, the same data is used to form an analytics report and then make some sort of forecasting regarding where the numbers are moving in the foreseeable future.
The data warehouse is a type of data storage designed for structured data with highly regulated workflows.
The highly structured nature of data warehouses makes it a natural fit for organizations that operate in clearly defined workflows and a reasonably predetermined scope.
The purpose of the big data warehouse is to gather data from different sources and organize it according to business requirements so that it is accessible for specific workflows (like analysis and reporting).
- The warehouse is designed by a database management system (DBMS) in the form of different containers. Each section is dedicated to a specific type of data related to a particular business process.
- The infrastructure of the warehouse revolves around a specific data model. The goal of the model is to transform incoming data and prepare it for further transformation and, subsequently, preservation.
As such, the data warehouse encompasses a broad scope of different types of data (current and historical). Such data as:
- Operational data like embedded analytics of the products,
- All sorts of website and mobile analytics,
- Customer data
- Transformed data such as wrangled datasets.
The main fields of use for data warehouse applications are business intelligence, data analysis, various types of reporting, decision support, and structured maintenance of business assets. Such as:
- Gain new insights by data mining databases;
- The same approach is viable for retrospective analysis;
- Performing market research or competitor research by plowing through large datasets of observatory data.
- Applying user behavior analysis and user modeling techniques to adjust business strategy and provide flexibility for the decision-making process (you can read about user modeling here).
In terms of business requirements, data warehouse architecture is a good fit in the following cases:
- To provide an accessible working environment for business analysts and data scientists.
- To accommodate high performance for an immense amount of queries for large volumes of data.
- To streamline the workflow to increase the efficiency of data exploration.
To enable strategic analysis with structured historical/archival data over multiple periods and sources.
Now, let’s take a closer look at the key differences between data lake vs data warehouse.
- Data Lake is for all sorts of unstructured, semi-structured, structured, unprocessed, and processed data. Because of this, it requires more storage space.
- Data Warehouse focuses on processed, highly structured data generated by specific business processes. This approach makes it cost-efficient in terms of using storage space.
The way data is handled is the biggest differential when comparing data warehouse vs data lake.
- The data lake is multi-purposed. It is a compendium of raw data used for whatever business operation currently needs.
- In contrast, data warehouses are designed with a specific purpose in mind. For example, gathering data for sentiment analysis or analyzing user behavior patterns to improve user experience.
Due to their unstructured, abstract nature, data lakes are difficult to navigate without a specialist at hand. Because of this, data lake workflow requires data scientists and analysts for proper usage.
This is a significant roadblock for smaller companies and startups that might not have enough resources to employ enough data scientists and analysts to handle the needs of the workflow.
On the other hand, Data Warehouse is highly structured, and thus its assets are far more accessible than a data lake. Processed data is presented in various charts, spreadsheets, tables – all available for the employees of the organization. The only real requirement for the user is to be aware of what kind of data he is looking for.
Due to its abstract structure, the data lake requires an intrinsic data processing pipeline with a configuration of data inputs from multiple sources. This operation needs an understanding of what kind of data is going in, and the scope of data processing operation to configure the scalability features of the storage correctly.
Data Warehouse needs a lot of heavy lifting to conceptualize the data model and build the warehouse around it. This process requires a clear vision of what the organization wants to do in the warehouse, synced with the appropriate technological solution (sounds like a job for a solution architect).
For the sake of security and workflow clarity – a data lake needs to be a thorough log protocol that documents what kind of data is coming from where and how it is used and transformed.
In addition to this, Data Lake needs external operational interfaces to perform data analytics and data science operations
Because of its accessibility, the central security component of the data warehouse is an access management system with a credential check and activity logs.
This system needs to delineate which data is open to who and to what extent (for example, middle managers get one thing, while seniors get a bigger picture, etc.).
Internet-of-things device data is a tricky beast.
- On the one hand, it needs to be available for real-time or near-real-time analysis.
- On the other hand, it needs to be stored all in one place.
The abstract nature of the data lake makes it a perfect vessel for gathering all sorts of incoming IoT data – (stuff like equipment readings, telemetry data, activity logs, streaming information).
The scope of big data provides data processing operation (“extract, load, transform” approach in particular) with a need to determine the value of specific information before embarking on further processing.
Data Lake architecture allows us to perform this operation faster and thus enables the faster progression of the processing workflow.
The “all at once” structure of the data lake is a good “playing field” for data scientists to experiment with data.
Analytics Sandbox leverages the freeform nature of the data lake.
Because of that, it is a perfect environment for performing all sorts of experimental research, i.e., shaping and reshaping data assets to extract new or different kinds of insights.
Historical data (especially in a long term perspective) often has insights for what the future holds.
This feature makes it valuable for all sorts of forecasting and predictive analytics.
Since historical data is less frequently in use, it makes sense to separate it from the current information, but retain similar architecture to keep at arm’s length if further analysis is required.
In some cases, it makes sense for an organization to streamline its data repository into a singular space with all types of data included.
In this case, the data lake serves as a freeform warehouse with different assets currently in use.
To keep things in order – this approach uses an internal tagging system that streamlines location and access to data for specific employees.
In certain cloud infrastructure approaches (you can read more about it here), front-end applications can serve through a data lake.
For the most part, this approach is a viable option if there are requirements for embedded analytics and streaming data back and forth.
A data lake can serve a virtualized outlet of a data warehouse designed for unstructured or multi-purpose data.
This combination is often used to increase the efficiency of the workflow with high data processing requirements.
Because of its abstractness, the data lake is a good platform for the transformation of the data warehouse.
It can be a starting point for the creation of the warehouse, or it can facilitate the reorganization of the existing warehouse according to new business requirements.
Either way, the data lake allows to preserve all data and provides a clean slate to build a new kind of structure on top of it.
Download Free E-book with DevOps ChecklistDownload Now
While data lakes are a great operational environment for IoT devices (for example, for individual sensor readings via Apache Hadoop), the data needs to be further processed and made sense of – and that’s a job for a data warehouse.
The role of a data warehouse, in this case, is to aggregate and filter the signals and also provide a framework on which the system performs reporting, logging, and retrospective analysis. Tools like Apache Spark are good at doing these kinds of tasks.
The availability of the Big Picture is crucial for strategic analysis. A combination of current and historical data enables a broad view of the state of things then and now in a convenient visualization.
Current data presents what is going on at the moment, while historical data puts things into context. Such tools as Apache Kafka can do this with ease.
The other benefit of merging live and historical data is that it enables a thorough comparison of then and now data states. This approach provides a foundation for in-depth forecasting and predictive analytics, which augments the decision-making process.
Web analytics requires smooth data segmenting pipelines that sort out incoming information and point out the stuff that matters inside of it.
It is one of the cornerstones of digital marketing and its presentation of relevant content to the targeted audience segments.
On the other hand, the very same approach is at the heart of recommender engines.
Presenting a continuity of product use is an important source of information to improve the product and its key aspects (such as UI). It is one of the ways to interpret embedded analytics.
Sessionization groups incoming events into a cohesive narrative and shows the statistics at selected metrics. Parallel processing tools cover its high-volume requirements like Apache Spark.
Both data lakes and data warehouses are complicated projects that require thorough expertise in the subject matter. On the other hand, there’s a need to bring together business requirements and technological solutions.
If you have a project like this or need help rearranging an existing project – call us, we can help you.
Want to receive reading suggestions once a month?Subscribe to our newsletters