Data Lake, Warehouse or Other Storage — What to Choose?


Data Lake, Warehouse or Other Storage — What to Choose?

Which one will deliver more insights to your business?

Written by:

YanContent Marketing Lead


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12 min read



Data is gathered at a tremendous rate. Whereas in the past companies treated its processing as nice to have in order to gain a competitive advantage, now it has become an absolute necessity to stay relevant and target the right consumers. According to the 2020 MicroStrategy report, 94% of surveyed companies stated that big data and analytics were a critical part of the continued growth of their businesses.

The benefits that these companies get are staggering. McKinsey did a report several years ago stating that companies using Big Data properly were 23 times more likely to grab customers over their competitors. In a separate report, McKinsey stipulated that retail players, powered by Big Data, could improve their operating margins by 60%.

On the contrary, a recent Gartner report stated that poor data management cost companies up to $15 million per year. Ironically, many of them may suffer even bigger losses, but they can’t provide precise figures because of poor data management.

To effectively manage and retrieve insights from the data, you need to choose the storage that will best fit your business. There are many types of those now, all with their peculiarities — a multitude that may cause some confusion.

You’re in the right place to resolve it. In this article we will tell you about each of the modern data storages, their pros and cons as well as some guidance on which one to choose to set your business upwind and not to a lakebed. Interested in settling this hydronic dilemma? So do we! Dive in!

What is a Data Lake?

The image depicting a data lake and the rivers of data, flowing into it

A data lake is a pool of both unstructured and structured data that’s coming from everywhere: from transactional types of data sets to web applications, social media sites, mobile applications, and other IoT data sets. Basically, the data is just captured and stored for later usage.

What competitive advantages does a Data Lake give?

  • As mentioned — the ability to store nearly all data types.
  • A data lake operates under a schema-on-read approach. This means that data is organized (schema) only when it’s being retrieved for usage (on read). Until then, it’s just dumped in the lake. Such an approach demands low upfront time & cost expenditures and thus makes storing data really cheap.
  • Low data input threshold also drastically increases flexibility. You are not bound by a certain way of data organization and reporting. Instead, you can configure the reports at your liking each time you refer to the lake.
  • The ability to store raw and unstructured data is perfect for deploying and training various AI & ML models.

Challenges with Data Lakes

The biggest challenges with data lakes are strong cataloging and security mechanisms. It is because the data adds in large volumes and without proper labeling can become hard to find or even unusable. This will lead to the creation of a so-called “data swamp” reducing the value of your investment to zero.

Another aspect of the problem is that the data stored can carry confidential information.

For instance, generic social media data can contain personally identifiable information (PII). Or a screenshot may have the details of some financial transaction.

In order to protect all this data, strong security mechanisms need to be developed including attack-resistant infrastructure, multiple authentication protocols and role-based access.

One more data lake’s drawback is that it demands specially trained personnel at least for the initial data extraction setup. Since the data is stored unstructured, such an action can only be performed by data engineers or other experts in data. After the extraction mechanism has been configured, though, everyone can work with a lake, including non-tech personnel.

Data Lake vs Data Warehouse

A data warehouse is another popular system. On contrary to a data lake, it employs a schema-on-write approach, meaning that all the data that enters it is subject to strict categorization and formatting. Thus, there’s no “raw” data in a warehouse, only that, organized in a defined way. Such an approach significantly limits its storing capabilities, making a data warehouse a primarily analytical system that only collects data for its further assessment.

The image depicting the working principle of a data warehouse

The main advantages of a data warehouse are:

  • High quality, consistency and accuracy of data due to the strong input filter and a predefined schema.
  • This schema also makes a data warehouse easily accessible to non-technical professionals from the start, allowing them to retrieve any necessary information and analyze it.

However, the are also disadvantages:

  • High upfront costs — building a data warehouse implies setting up sophisticated data input mechanism that takes time and money to build.
  • Rigidity — any changes to this mechanism will incur significant expenses to make.

You will wonder which option is better for your business. There isn't an unequivocal leader here because they just have drastically different structures designed for different situations. This leads us to a logical conclusion that the choice between the two should be made depending on the business objectives and technical capabilities: the aim of data collection, budget, tech stack, infrastructure’s capacity, etc.

Here’s a table with hand-on comparison that will hopefully help you make up your mind on which architecture to use.

A comparison table between a data lake and a data warehouse

But what if none of the storages suit you in their original form? There is a solution — combining them.

Data Lake and Data Warehouse Combined

Some companies are using both storages to embrace the full potential of Big Data. For example, data warehouses are used for generating business insights while data lakes serve solely as central repositories and AI training polygons. Other businesses combine the two storages to unlock new value streams and revenue generating opportunities. Here are the two possible scenarios.

  1. Raw data flows into a data lake. Then, it is explored and analyzed to discover business value. This data is then moved to a warehouse, organized in a definite way and used for high-quality reporting.
  2. The primary storage is a data warehouse. The data is initially assessed according to its pattern and then put in a data lake for performing different analysis scenarios that can also include AI & ML.

Data Lakehouse

The ultimate blend of these storages is called a data lakehouse. It unites functionalities of a data lake and data warehouse in one solution, basically providing a cheap scalable storage that supports data types (raw, semi-structured and structured) while also applying strict data governance and quality mechanisms. This allows to both retrieve intelligence insights from and train Ai & ML models on the same data, making it the most profitable option for businesses.

Other benefits of this solution include:

  • Reduced redundancy and data travel — the all-in-one solution removes the need to run multiple storages.
  • Lower costs.
  • Faster operation speed and insights retrieval.
  • Simplified data governance.
  • Direct access to analytical tools.

Other Data Storage Types

Data lake and data warehouse aren’t the only storages present on the market. What are other types and how are they different?


The image depicting the structure of a database

A database is a classical storage designed to accumulate and easily retrieve data. The concept unites it with a data lake, but there is a major difference: the latter is designed for Big Data, meaning it can serve as a company-wide storage. Whereas databases are typically set up for individual applications which limits their use areas.

A similarity with a data warehouse is in the fact that they both store structured data. However, a database only represents factual real-time data from a single source while a warehouse contains highly summarized current and historical transactional data from a variety of sources, which may not be up-to-date. Different are their purposes as well: a database is designed to store and represent data whereas a warehouse is used for analyzing it and deriving intelligence.

Data Mart

The image depicting data marts as interim storages on the way to a data warehouse

This is a lighter version of a company data warehouse designed specifically for individual departments or business lines like marketing, accounting, etc. It drags data from fewer sources (mainly this unit-wise but a central data warehouse can also be a donor). This makes a data mart an own source of truth for respective departments and allows them to retrieve required intelligence faster, not depending on central analytics for making tactical decisions.

Operational Data Store (ODS)

The image showcashing an operational data store (ODS) as a 2-nd element in the chain "data source - ods - staging - data warehouse"

An Operational Data Store is a temporary gateway between data sources and its central location. It is a first step in the ETL (Extract, Transform and Load) or ELT (Extract, Load, Transform) process, where the raw data from different springs is consolidated before any further actions. It updates very frequently (daily or even hourly) and each new retrieval overwrites the old information.

For this reason, an ODS is where operative BI (Business Intelligence) and decision-making are unfolded: analyzing real-time data ensures you don’t miss out on sudden opportunities or react rapidly to unexpected obstacles.

Meanwhile, the data passes on through the ETL funnel and gets into a central repository for company-wide historical decision-making.

Data Hub

The image showcasing a data hub as a data delivery system that intertwines all company's storages

A Data Hub isn’t a type of storage - it’s an overall architectural pattern of data governance. This is a single source of truth in an organization, i.e. a main pipeline for data, from which it is distributed to various endpoints. A data hub’s aim is to unify and standardize organization data management, proactively applying governance mechanisms, harmonizing the data and preparing it for easy digestion by the acceptors. Schematically, a data hub is everything that is in-between the sources and acceptors.

With that being said, a data hub unites all company data management into a single network, connecting all the storages it might have, including data warehouses and data lakes. This way, a hub supports both structured and unstructured data to make the acceptors properly ingest it.

At the same time, data lakes and data warehouses can act as the sources of data instead of the acceptors (for example for performing analytics or training AI models). In that case, the job of a data hub will be to extract the necessary data and prepare it for correct decoding by the target systems.

In The End

Choosing a proper data storage for an organization is a tough affair with so many options to consider and so much information to account for. We hope that our article made it easier for you and helped determine what will be the most beneficial for your business. 

In any case, whether you’ve made up your mind or not, bringing in a seasoned technology partner can greatly cut down on costs and time to implement a system. We know that every business executive wants it at the end of the day, so we are glad to offer you our help.

We have helped lots of our Clients tame Big Data and put it into service, unlocking the capabilities, simply unreachable with their legacy data systems. Such a business giant as Bayer has paid us a testimony, granting a 5-star review on Clutch. You can read it here as well as the more detailed case study here. On top of that, we have one of the strongest Big Data teams on the market, which notably differs us among the competitors.

If you would like to make your business one of those lucky ones that skyrocketed their figures using data — fill in the form below and our experts will contact you and answer everything that interests you. You can also learn more about how we work in our cases and blog sections.

It’s time to embrace Big Data!

Why Senla?

Strong Big Data Teams

We understand the great importance of niche Big Data services and made it one of our priority service lines. We regularly host educational courses on the subject and invite the best-performing students to join us. Thanks to this, we’ve assembled powerful teams that have solid and relevant expertise.

Many Successful Big Data Projects

Apart from one of the best-in-class theoretical bases, our developers often apply it in the field, keeping it relevant with ever-evolving market requirements and gaining strong domain expertise.

Reducing costs

With us you can optimize up to $30,600 in talent, hiring, support, and retention, and up to $16,200 in administrative expenses per engineer.

Reduced bureaucracy

By partnering with us, you free yourself from extensive hiring and onboarding routine and take enormous strain off you, your in-house staff, and your resources.

Frequently Asked Questions

How soon can we start cooperation?

Our experts can get to work in as little as 5-10 business days after the introductory call, depending on the engagement model that you choose.
First is an introductory call with our BD team to clarify the project needs. Upon your request we sign an NDA. After the evaluation, we offer a preliminary solution (CV, timelines, etc.). If everything is OK, our staff gets to work.

What if my storage requirements change during development?

No problem. We support all software development methodologies, including Agile. So, if you need a different configuration to your data lake — be sure we’ve got it. The same applies to scaling — if you need more hands, just give us a month’s notice.

Can you help me with my data management after the project is finished?

Sure! We strive to form partnerships, not projects. Our cooperation, based on lifetime warranty, doesn’t stop with the development. If anything happens, you are still covered. Even if you decide to implement another data lake. Additionally, we can maintain your storage upon a request.

I've already started development with a different vendor but I’m not happy with the results. Can you help?

If you are not satisfied with your existing partnership, you can transition the work to us. We will take the ball and run with it.

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