Cloud Data Storage Patterns: Data Warehouse vs. Data Lake vs. Data Lakehouse
Cloud Data Storage Patterns: Data Warehouse vs. Data Lake vs. Data Lakehouse
We live in an era dominated by the cloud. Each day more technologies and terms emerge and evolve. Rapid development and innovation are catalyzing data generation and it grows by the day. Knowing fundamental things such as data warehouse vs. data lake vs. data lakehouse comes in handy in the longer run.
Businesses are now investing in new technologies to store and manage their data and capitalize on it in the most efficient way possible. Real-time data brings numerous benefits but storing it is the real challenge.
There are so many data storage systems available that it can be quite stressful to understand the difference and determine the suitable one.
That is exactly why in this article, we will compare the main three data storage architectures: data warehouses vs data lakes vs data lakehouses.
What is a Data Warehouse?
A data warehouse can be defined as a repository for massive amounts of structured data from different sources. It is precisely designed to store structured, curated data.
This works by organizing datasets into tables and columns. Once your data is available in the data warehouse it is made conveniently available for users for dashboards, reporting, and business intelligence.
Normally, data falls under three categories:
- Structured data – This is the most quantitative data and is incredibly organized. For instance, names, addresses, contact, social security numbers, birthdays, geolocation, etc.,
- Semi-structured data – Semi-structured data can be defined as a combination of both; structured and unstructured data. The frameworks are loosely defined. For instance, emails and addresses for sender/recipient, but the message body could contain anything.
- Unstructured data – Unstructured data is qualitative. There are no defined frameworks and it is rather difficult to perform search tasks. Examples of unstructured data can be videos, photos, reviews, audio files, etc.,
Data within a data warehouse is processed for sorting, validation, aggregation, reporting, classification, summarization, and analysis. Having a data warehouse enables businesses to conveniently access their data and analyze it to develop workable insights.
Data Warehouse Architecture
When it comes to designing data warehouses, a three-tier architecture is the most popular approach. It consists of:
- Bottom Tier – This is the staging area and also the database server of your data warehouse. It is used to load data from multiple sources. An
- Middle Tier – Middle tier is a server for OLAP (Online Analytical Processing) that takes the data and reorganizes it into multidimensional formats for faster calculation.
- Top Tier – Finally, we have the top tier where the APIs and frontend tools are located for working with the data.
Components of Data Warehouse
To provide insights, a data warehouse normally comprises four key components:
- Central Database: The central database can be classified as the backbone of any data warehouse. This database embodies all the data organized into tables that grab related objects and group them.
- Data Integration Tools: Data integration tools serve the purpose of pulling data from multiple sources and transforming it so it may fit within your data warehouse. The traditional approach implemented here is ETL (Extract, Transform, and Load).
- Metadata: You might already be familiar with this term. It is the “data” about the data in the warehouse and is normally used to create context and organization.
- Data Access Tools: These include application development tools, query tools, data mining tools, and even OLAP tools. Data access tools enable you to interact with all the data stored within the data warehouse.
What is a Data Lake?
A data lake is also a centralized repository for raw data. It commonly collects undefined raw data in its purest form. You should know the key difference between a data lake and a data warehouse – a data lake stores the data without arranging it in any sort of logical relationships (schemas).
That is to say, data is quickly dumped into the data lake before being processed for sorting, validation, aggregation, summarization, or classification.
Nevertheless, this is the reason why more sophisticated analytics are enabled. A data lake pulls in:
- Transactional data,
- Device Logs and IoT Sensors,
- Images, Binary, PDF, Audio Files.
Data Lake Architecture
Unlike a data warehouse, a data lake applies a flat architecture for data storage. The key components of a data lake architecture are:
- Bronze Zone – Dedicated zone for ingested data. It is either stored as-is for batch patterns or aggregated sets of data for streaming workloads.
- Silver Zone – Point where your data is filtered and enriched to enable exploration per business requirements.
- Gold Zone – The fanciest place of all, where well-structured data is stored to implement business intelligence and machine learning algorithms.
- Sandbox – Finally, the sandbox is where data can be experimented on for validating different hypotheses or performing tests.
Data lakes only store raw data that is not useful on its own since they do not inherently contain analytics capabilities.
Components of Data Lake
Data lake architecture has five main components. You can even remember them using the acronym ISASA. So what does ISASA mean?
- Ingest – Data migration through batch processes or APIs.
- Store – When you store data ingested from multiple sources into a single repository and no silos are used.
- Analyze – You can analyze the data to explore relationships and even make accurate forecasts.
- Surface – This component refers to presenting the findings of the analysis in simple ways. For instance, graphs, charts, or practical insights.
- Act – Finally, your data is ready to be worked on to address the business requirements.
Compared to data warehouses, data lakes are made upon inexpensive object storage. In other words, you get simple, affordable, and scalable storage.
What is a Data Lakehouse?
Finally, the third cloud data storage option is data lakehouse. This is a comparatively recent data management architecture that merges open format, flexibility, and affordability of data lakes with management, accessibility, and even advanced analytics support.
If you take marketing out of the equation, the core concept of a data lakehouse is to pump up a data lake with computing power. Therefore, it can be said that it is a solution concept that mixes elements of a data warehouse and a data lake.
The data structure of data warehouses is implemented and management features are taken from data lakes. The management features are more cost-effective, so it enables you to get the best of both without the cost.
Data Lakehouse Architecture Design
Generally, a data lakehouse architecture consists of five layers:
- Ingestion layer – Similar to the data lake, the ingestion layer is responsible for acquiring data from numerous data sources and delivering it to the storage layer.
- Storage layer – As the name suggests, the data is stored in open formats. You can also refer to this as a “data lake” and it is segregated from the computing layer.
- Computing layer – The computing layer gives the business warehouse capabilities. This includes support for metadata management, schema enforcement, indexing, and ACID (Atomicity, Consistency, Reliability, and Durability) transactions.
- APIs layer – Simple role of allowing access to data assets.
- Serving layer – Finally, the serving layer exists to support multiple workloads. This includes reporting to data science, business intelligence, and machine learning.
The data lakehouse is a fresh advancement in the data analytics world. It addresses both data warehouse constraints and data lake challenges.
Comparative Analysis: Data Warehouse vs. Data Lake vs. Data Lakehouse
After fully understanding the data warehouse, data lake, and data lakehouse, it is now time to run a comparative analysis of the three.
The differences between the storages can be summarized as follows:
- Data Warehouses allow easy querying and usage but data is difficult to store.
- Data Lake makes ingestion easy in contrast to data warehouse but you will run into problems while using and querying the data.
- Data Lakehouse is the optimal solution as it combines the best of both; data warehouse and data lake.
Here’s a diagrammatical representation to give you a clearer image:
To strengthen the comparison further, the table below provides an outline of the main difference between data warehouse vs. data lake vs. data lakehouse.
Side-by-Side Comparison: Data Warehouse vs. Data Lake vs. Data Lakehouse
|Parameters||Data Warehouse||Data Lake||Data Lakehouse|
|Purpose of Data||Business intelligence or analytics.||Machine Learning and AI workloads.||Both, from the data lake and warehouse.|
|Data Type||Structured||Unstructured||Structured and Unstructured|
|Users||Business Professionals||Data engineers and data scientists||Both, business professionals and data teams.|
|Quality of Data||Highly organized data and reliable.||Low quality, raw data, and not so reliable.||Raw and organized data. Offers high quality with built-in data governance.|
|ACID Compliance||Compliant – highest level of integrity guaranteed.||Non-compliant – deletes and updates are complex operations.||Compliant – ensures consistency as multiple sources simultaneously write/read data.|
|Storage||expensive and time-consuming.||Affordable, flexible, and rapid.||Affordable, flexible, and rapid.|
|Schema||Write Schema||Read Schema||Schema Enforcement|
Picking a data storage solution from the three would still not be as easy. However, here are a few rules of thumb to give you an idea:
Go for a data warehouse if:
- You have the exact questions and are already aware of what analytics results you need on a daily.
- You are in some highly regulated industry such as insurance or healthcare and have to comply with far too extensive regulations on reporting.
- Simple historical analysis can help you address your reporting requirements and KPIs.
Go for a data lake or data lakehouse if:
- Your organization is mature in data and wants to benefit from machine learning technology.
- You are after exploratory analysis or experimental analysis.
With that said and done, be sure to consider your budget and time constraints. Data lakes prove to be more cost-effective and faster to build in comparison to data warehouses.
Takeaway – Data Warehouse vs. Data Lake vs. Data Lakehouse
When it comes to data warehouse vs. data lake vs., data lakehouse, there is a single key differentiator and that is organization. Whether you want to modernize legacy systems to support machine learning and performance improvement or create a data storage solution. The right answer won’t be that easy.
There is a lot that needs to be done before all the key differences can be explored. That is mainly due to the reason that benefits, costs alongside offerings, and pricing models from vendors are evolving at a rapid pace. As it stands, it can be concluded that data lakehouse is the right choice since it merges the best of both data warehouse and data lake.
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