Data comes at us fast and in many forms. These different forms can include structured, semi-structured, and unstructured data and many people do not realize that a data warehouse and a data lake handle the data differently.
The Modern Data Estate: Data Lake vs. Data Warehouse
Article from | MCA Connect
A modern data estate should provide multiple methods of ingesting and storing the various data that businesses generate. Data comes at us fast and in many forms. These different forms can include structured, semi-structured, and unstructured data and many people do not realize that a data warehouse and a data lake handle the data differently. Let’s look further at these different types of data:
- Structured – traditional databases such as the transactional database for your ERP or CRM system with formal column and table definitions
- Semi-Structured – files such as XML or JSON that are self-describing with tags for elements and hierarchies
- Unstructured – images, video, audio, and other binary data
Traditional data warehouse designs have been around for many decades while the concept, or at least the term, data lake is a somewhat newer construct. Each of these has a place in your organization’s data estate.
The Data Warehouse
As we can see above, data sources can be very diverse and have different data representations, which can lead to divergent information. In addition, the large variety of schemas and structures in data sources makes it difficult to obtain consolidated information when a complete snapshot of the data is required from all business sub-systems. In general, this is the main reason for the emergence of Data Warehouse solutions.
A data warehouse is a formal design, frequently based on design guidelines that implements for formal ETL (Extract-Transform-Load) process to consume raw, structured data sets and load them into a model designed for reporting. Data warehouses are built on relational databases like Azure Synapse, previously Microsoft SQL Server. Azure Synapse is designed to store structured data into tables with traditional rows and columns but does have the capability to store semi-structured data like XML and JSON.
The Data Lake
A data lake flips the concept of ETL on its head and implements an ELT (Extract-Load-Transform) process. Ingesting data into the data lake is essentially just throwing everything you think may be valuable at some point into a large storage area regardless of data type or structure. Data lakes can store structured, semi-structured, and unstructured data. Data lakes delivered in Microsoft Azure are built on storage accounts with Data Lake Storage Gen2 enabled when creating the storage account.
The thought behind a data lake is you want to consume all the data and will sort through it at a later point while the data warehouse requires identifying the value upfront with significant investment developing the ingestion. Due to the heavy, upfront investment typically required to develop a data warehouse, if it is later determined that you need data that wasn’t brought in initially, there is a risk the source data is no longer available and potentially gone forever.
Purpose: undetermined vs in-use
The purpose of individual data pieces in a data lake is not fixed. Raw data flows into a data lake, sometimes with a specific future use in mind and sometimes just to have on hand. This means that data lakes have less organization and less filtration of data than their counterpart.
Processed data is raw data that has been put to a specific use. Since data warehouses only house processed data, all of the data in a data warehouse has been used for a specific purpose within the organization. This means that storage space is not wasted on data that may never be used.
Accessibility
Accessibility and ease of use refers to the use of data repository as a whole, not the data within them. Data lake architecture has no structure and is therefore easy to access and easy to change. Plus, any changes that are made to the data can be done quickly since data lakes have very few limitations.
Data warehouses are, by design, more structured. One major benefit of data warehouse architecture is that the processing and structure of data makes the data itself easier to decipher, the limitations of structure make data warehouses difficult and costly to manipulate.
The Benefits of Both
Data lakes are a cost-effective way to store large amounts of data from many sources. Allowing data of any structure reduces cost because data is more flexible and scalable as the data does not need to fit a specific pattern. However, structured data is easier to analyze because it is cleaner and has a uniform schema to query from. By restricting data to a schema, data warehouses are very efficient for analyzing historical data for specific data decisions. Both a proper data warehouse and a data lake are critical to the future success of your organization and belong in your modern data estate.
What is a Data Estate?
Establishing a modern data estate is a foundational step toward digital transformation. A modern data estate enables timely insights and decision-making across all your data and sets the foundation for AI. A data estate is all of the data an organization owns. When you migrate this data to the cloud or modernize your environment on-premises you can gain important insights to fuel innovation.
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Microsoft Dynamics 365 Pre-Built Data Warehouse, DataCONNECT
Building a data warehouse can be very expensive and time-consuming to properly review your source systems, design a data model, and create the necessary ETL to process it. MCA Connect developed our DataCONNECT Data Warehouse solution for Microsoft Dynamics AX, Dynamics 365 Finance, and Customer Engagement. This solution greatly accelerates the timeline for the delivery of a comprehensive data warehouse solution while reducing implementation costs. It is also a great way to start building your comprehensive data estate.
DataCONNECT can fuel organizations with fast, accurate information, giving them the ability to predict, adapt and shape operations with precision. You will be able to quickly pull validated data into forecasting models, so you can begin your planning cycles for areas of your business. If you’d like to learn more about how the DataCONNECT Data Warehouse or a data lake can help your company store big data, contact us. One of our experts will be glad to guide you in the right direction.
The content & opinions in this article are the author’s and do not necessarily represent the views of ManufacturingTomorrow
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