The term “Data Lake”, “Data Warehouse” and “Data Mart” are often times used interchangbly. But what are exactly the differences between these things? This post attempts to help explain the similarity, the difference and when to use each.
A data lake is the place where you dump all forms of data generated in various parts of your business: structured data feeds, chat logs, emails, s (of invoices, receipts, checks etc.), and videos. The data collection routines does not filter any information out; data related to canceled, returned, and invalidated transactions will also be captured, for instance.
1- Your organization is so big and your product does so many functions that there are many possible ways to analyze data to improve the business. Thus, you need a cheap way to store different types of data in large quantities.
Eg. Twitter in the B2C space (They have text (Tweets), s, Videos, Links, Direct Messages, Live Streams, etc.), and Square (B2B) (Transactions, Returns, Refunds, Customer Signatures, Logon IDs etc.).
2- You dont have a plan for what to do with the data, but you have a strong intent to use it at some point. Thus, you collect data first and analyze later.
Also, the volume is so high that traditional DBs might take hours if not days to run a single query. So, having it in a Massively Parallel Processor (MPP) infrastructure helps you analyze the data comparatively quickly.
A Data Warehouse is multi-purpose and meant for all different use-cases. It doesn’t take into account the nuances of requirements from a specific business unit or function. As an example, let’s take a Finance Department at a company. They care about a few metrics, such as Profits, Costs, and Revenues to advise management on decisions, and not about others that Marketing & Sales would care about. Even if there are overlaps, the definitions could be different.
Organizations today have access to ever-increasing volumes of data from various sources like applications, vendors, Internet of Things (IoT) sensors, and third parties. However, to derive practical benefits, they need to process, filter and analyze the raw data to identify patterns and trends. At the same time, rigid data protection and governance requirements necessitate adhering to security and regulatory compliance practices.
To achieve their data analytics goals, businesses use different tools and solutions for collecting, processing, analyzing and storing data. The three most common data storage solutions are:
- Data Lake
- Data Warehouse
- Data Mart
While these three serve the common goal of storing data, they differ in architecture, use cases, and capabilities. Determining which one or combination works for your requirements depends on several factors. Through this article, we will cover:
- The similarities between data lakes, warehouses and marts
- Key differences between data warehouses and data marts
- Key differences between data warehouses and data lakes
- When to use which data storage solution?
- How AWS and IBM provide data storage solutions?
Similarities Between Data Lakes, Warehouses and Marts
While data lakes, data warehouses and data marts have distinct characteristics, they also share some common capabilities and benefits:
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Secure storage for business data analytics All three solutions allow secure storage of business data to enable analytics and business intelligence initiatives. For example companies can store data from across departments lines of business, external sources, and legacy systems.
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Scalable and unlimited data volume Organizations can store unlimited data volumes in data lakes, warehouses and marts for as long as required. The storage scales as per changing business data needs.
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Data integration and silo elimination: These solutions help break down data silos by facilitating integration of information from multiple business processes and sources. For instance, a data lake or warehouse can hold data from CRM systems, ERP databases, web server logs, social media APIs, partner data feeds, and more.
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Batch and real-time data analysis: Users can perform both historical analytics on batch data, as well as real-time analytics on streaming data stored in these repositories. The integrated data supports pattern identification, forecasting, predictive modeling, and other analytical use cases.
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Cost-efficiency You only pay for the storage volume you use in cloud-based data lakes, warehouses and marts The solutions are more cost-effective than traditional on-premise infrastructure and storage
Key Differences Between Data Warehouses and Data Marts
While data warehouses and data marts are relational databases optimized for analytical SQL queries, their architecture and usage differ. Here are some key points of difference:
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Data sources: A data warehouse integrates data from multiple sources including internal systems and external feeds. Data marts typically have fewer sources, usually extracting data from a portion of an existing enterprise data warehouse.
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Focus: Data warehouses have a broader focus and centrally store data from across departments for organization-wide analytics. Data marts are decentralized, department or team-specific databases filtered from a warehouse.
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Utilization: Data warehouses have a longer lifespan and support multiple projects and users. Data marts solve specific use cases and may be terminated after project completion.
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Design approach: Data warehouses follow a top-down approach with upfront architecture planning. Data marts are designed bottom-up as data schema details are already known beforehand.
| Characteristics | Data Warehouse | Data Mart |
|---|---|---|
| Scope | Centralized, multiple subject areas | Decentralized, specific subject area |
| Users | Organization-wide | Single department or community |
| Data sources | Many sources | Single source or portion of warehouse data |
| Size | Very large – 100s of GBs to PBs | Small – up to 10s of GBs |
| Design approach | Top-down | Bottom-up |
| Data detail | Detailed, complete data | May contain summarized data |
Key Differences Between Data Warehouses and Data Lakes
While data warehouses contain structured data, data lakes are centralized repositories for storing any data at any scale, structured or unstructured. Here are some key differences:
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Data sources: Data lakes can take in data from unlimited sources in raw format. Data warehousing requires defining schema before loading only structured data.
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Preprocessing: Data warehouses need data to be cleaned and transformed before storing. Data lakes allow loading data first, then transforming as needed.
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Data quality: Data quality in warehouses is higher due to upfront processing and de-duplication. Data lakes may contain duplicates and errors if no preprocessing is done.
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Performance: Data warehouse architecture optimizes query performance for business users. Data lakes prioritize lower storage costs over performance.
| Characteristics | Data Warehouse | Data Lake |
|---|---|---|
| Data | Relational, structured | Any data including unstructured |
| Schema | Defined upfront (schema-on-write or schema-on-read) | Written at analysis time (schema-on-read) |
| Price/Performance | Fast querying with higher costs | Slower querying with low storage costs |
| Data quality | Highly curated | May or may not be curated |
| Users | Business analysts, data scientists | Business analysts, data engineers, data architects |
| Analytics | Batch reporting, BI, visualizations | Machine learning, exploratory analytics, big data |
When to Use Data Lakes, Warehouses and Marts?
In most cases, organizations utilize a combination of data lakes, warehouses and marts for their analytics needs:
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Data lakes provide the most flexibility for storing and analyzing all data types affordably. Different teams can use data lakes as data sources for their preferred tools. You can quickly make raw, unstructured data available for exploration without upfront modeling.
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Data warehouses are the preferred solution if the focus is on analytics involving high volumes of structured, relational data. You can store data from transactional systems, operational databases and line of business systems optimized for fast SQL queries.
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Data marts enable specific departments to create subsets of data from a warehouse containing only information relevant to their use case. For example, sales could create a customer focused data mart for campaign management, while finance maintains a mart for accounts and billing.
The technology choice depends on your volume and variety of data, frequency of use, query performance needs and cost considerations. Many organizations combine the strengths of data lakes, warehouses and marts in a hybrid solution.
How Can AWS and IBM Help With Data Storage?
AWS and IBM offer a wide range of analytics and storage solutions suitable for data lakes, warehouses and marts:
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Amazon Redshift provides petabyte-scale data warehousing with integration across operational databases, data lakes and business intelligence tools on AWS.
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IBM Cloud Pak for Data is a fully-managed platform for data and AI. It allows creating data marts, data warehouses and data lakes.
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AWS Lake Formation allows building secure data lakes in days for access to data analytics and machine learning.
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IBM Watson Studio provides a suite of tools for data scientists, application developers and subject matter experts to collaboratively build machine learning and data science models.
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Amazon S3 enables building scalable data lakes on AWS for analytics and machine learning workloads.
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IBM Db2 Warehouse offers an elastic cloud data warehouse on IBM Cloud with independent scaling of storage and compute.
Both AWS and IBM provide enterprises with the capabilities to build solutions incorporating data lakes, warehouses and marts based on business
Here are Top 5 Differences between Data Lake and Data Warehouse!
Data Mart is often mistaken with data warehouses, but the two serves completely different purposes, and here is how:
1. Assisting different data types: A data warehouse usually consists of data that has been extracted from transactional systems and is made up of quantitative metrics and the characteristics that describes them.
A data lake system supports non-traditional data types, like web server logs, sensor data, social network activity, text and s. These non-traditional data sources have largely been ignored like wise, consumption and storing can be very expensive and difficult.
2. User Support: A data warehouse is an ideal use-case for users who want to evaluate their reports, analyze their key performance metrics or manage data set in a spreadsheet every day. Hence, a data warehouse is ideal for “operational” users, as it is simple and it’s built to meet their needs.
A data warehouse can also support users who do more analysis on data. They use data warehouse as a go-to source for data integration, data preparation and data analytics. Users may also use data warehouse to do deep analysis, which may create totally new data sources based on research. These users are mainly ‘Data Scientists’ and use advanced analytical tools like predictive modeling and statistical analysis.
The data lake system supports all of these users well. Let’s say for example, a data scientists can use their data lake system and work with very large and different data sets that they require, while their business users can make use of a more analytical view of the data provided for their use.
3. Maintaining Data: During the creation of a data warehouse, a large amount of time will be spent on analyzing data sources and understanding business process and composing data. A large part of this procedure involves making decisions about which data to include and which data to exclude.
However, data lakes maintains ALL data. Not just data that is used today but data that may want to be used someday. Data can also be kept for a long time so that we can go back anytime and want to analyse such data again.
This approach is only possible because of the hardware capability of a data lake, which usually differs from what is used in a data warehouse.
4. Adapting to change: A good data warehouse design can adapt to change very well, because of the complexity of the data loading process and the work done to make analysis and reporting easy. These changes, however will require plenty of time and resources from such developers.
Many corporations today question the time consumed for the data warehouse team to adapt in their system. This ever increasing time has given rise to the concept of self-service business intelligence.
On the other hand with data lake, as all of the data is stored in a raw form and it’s always accessible to someone who needs to access it. Users are given the power to explore data beyond the capability of exploring data in a data warehouse.
5. Speedy Insights: This difference is based on the result of the 4 components mentioned above. Data lakes contain all data and data types, which enables users to access data before it has been transformed and structured, this will allow users to get their results faster than a traditional data warehouse approach.
However, this approach may not be as convenient as it sounds. The typical work done by the data warehouse team may not be the same for all of the data sources that is required to do an analysis. This in fact will leave users to explore and use data that they see fit, but a business user may not want to do that work. A business user use-case, is just to get access to reports and KPI’s
With data lake, these operational reports will make use of a more structure view of the data in the data lake, which stimulate what they have always had before in the data warehouse. The difference with this approach is that primarily as metadata which sits over the data in the lake instead of physically rigid tables that require a developer to change.
The Approach you should choose?
That’s a tricky question. If you currently already have a well developed data warehouse, we certainly don’t advice removing it and starting over. However, we certainly advice you to implement a data lake alongside your data warehouse. Your data warehouse can proceed to operate as usual and you can start filling your data lake with new data sources. You can also use it for the collection of your warehouse data that you can roll off and keep it available for your users with access to more data. As your warehouse matures, you can move all your data to your data lake or you may continue the same process. Especially, if you are are starting down the path to build a centralized data platform, it’ll be a better idea to consider both approaches.
While a data-warehouse is a multi-purpose storage for different use cases, a data-mart is a subsection of the data-warehouse, designed and built specifically for a particular department/business function.
Some benefits of using a data-mart:
- Isolated Security: Since the data-mart only contains data specific to that department, you are assured that no unintended data access (finance data, revenue data) are physically possible.
- Isolated Performance: Similarly, since each data-mart is only used for particular department, the performance load is well managed and communicated within the department, thus not affecting other analytical workloads.
Data Mart vs Database vs Data Warehouse vs Data Lake Explained
What are data warehouses & data lakes?
Data warehouses, data lakes, and data marts are different cloud storage solutions. A data warehouse stores data in a structured format. It is a central repository of preprocessed data for analytics and business intelligence.
What is the difference between a data mart and data warehouse?
The reason is because a data warehouse is structured and can be more easily mined or analyzed. A data mart, on the other hand, contains a smaller amount of data as compared to both a data lake and a data warehouse, and the data is categorized for a specific use or by a specific demographic or business unit.
What is a data mart vs data lake?
Data marts offer the convenience of having just the relevant data for a specific team’s needs, making it easier and quicker for them to get insights without sifting through the entire data warehouse. In the context of data warehouse vs data lake, the main thing to remember is the type of data you’re dealing with and the flexibility you need.
Are data lakes better than data marts?
Data marts are very specific, allowing for fast, effective analytics of relevant summarized information. Data lakes are better for broader, deep analysis of raw data. Data lakes are more an all-in-one solution, acting as a data warehouse, database, and data mart. A data mart is a single-use solution and does not perform any data ETL.
