Data Lake Vs. Data Warehouse: Key Differences, Benefits and Challenges
Data plays a vital role in the research and analysis function, particularly the large amount of data that provides detailed insights into the representations. Organizations collect large amounts of data from various platforms, channels, or resources to develop their business and provide services based on the data availability. Data could be in any format, size, and value referred to as Raw data / unstructured data. In recent days collection of data volume has kept increasing; hence to handle the volume, there is a need for additional systems or tools which could manage and helps with interpretation.
Here the role of a Data Lake or Data warehouse facilitates handling a huge volume of data. These repositories have similar core functions, like housing data for analysis and business reporting. Still, both architecture has their differences in terms of their purposes, structure, the types of data they store, the source of the data, and who can access it.
Generally, data generated by CRM, ERP, HR, financial applications, and other sources are stored in these repositories. Once these data are collated into the repository, they can be used in data analytics tools like Power BI, Tableau, etc., to identify the trends and gain insights to make business decisions (for example, data on the sales report of a store in the last six months helps to identify the sales trend with the collected historical data and gain insights on the future trend).
A data lake is a centralized storage repository that can store data in a raw unstructured form, semi-structured form, or structured form.
Data can be stored in its native format without fixed file size limits. Data Lake stores large amounts of current and historical data in various formats like JSON, CSV, emails, PDFs, Images, audio, video, etc., without imposing schema (i.e., a formal structure of how the data is organized).
Data Lake uses the ELT (Extract Load Transform) process, which is ideal for users who need in-depth analysis.
Data Lake Architecture is a flat architecture that uses object storage to store data. Object storage stores every data element with a unique identifier and metadata tags. This makes it easier to locate and retrieve data across regions.
Data collected from multiple sources are placed in a single place. Hence it should be stored in a usable form and have some rules and regulations to maintain data security and accessibility. If it is not done, it will be very difficult to distinguish between the data you want and the data you are retrieving, resulting in a data swamp.
Data Lake is managed by data engineers or scientists who design, build and maintain the data pipelines that bring data into Data Lake.
Key Benefits Of Building A Centeralized Data-Lake
- Data Lake allows ingesting raw data in different formats (Structured, Unstructured, Semi-structured).
- Schema is defined after the data is stored, which results in high agility, and data can be captured easily.
- Storage Cost is low.
- Provides better querying results.
- It transforms data at the end of the process (schema on reading), which is ready for application purposes (ex: SQL analytics, Power BI, or any other machine learning).
- A single place to have all data with the collation of multiple data resources.
- Diversify in data sources & formats.
- User-friendly, especially for machine learning, real-time analytics, etc.
Challenges When Starting Data Lake Projects:
- The quality of data standards is limited as the data may or may not be curated.
- Unclear data distinguishes which results in most data lakes into data swamps.
- Reliability Issues.
- Slow Performance.
- Lack of Security features.
A Data warehouse is a repository of structured, pre-processed filtered data from a different database or a data lake.
Data Warehouses stores data in a hierarchical format using folders and files. ETL (Extract, Transform, and Load) processes arrange data in multi-dimensional formats so analytics workflows using Data Warehouses can start.
Data warehouse involves extracting, cleaning, and converting the data to warehouse format, consolidating, and storing in a warehouse.
Business specialists and Data Analysts can generate reports and create dashboards using the data stored in a Data Warehouse.
Benefits of a Data Warehouse for Your Data-driven Organization
- Data stored in the data warehouse remains secure and reliable.
- Data can be easily retrieved and managed.
- Identify errors and corrects them before feeding the data into the warehouse.
- Ensure data quality and consistency.
- It can be integrated into the CRM system easily.
- Information to the users will be in a simpler format that business users can easily understand. Hence it saves time and increases productivity.
Data Warehousing and its Challenges
- The time-consuming process is when inputting raw data and cleaning it. i.e., under-estimating ETL process time.
- Ownership concerns arise as it is a Central repository, and departments may hesitate to share their data.
- Requires large amounts of data resources to manage data from multiple resources, resulting in higher costs.
- Hidden errors from the source systems that feed the data into the data warehouse may be undetected and identified after years.
- Data Homogenization- Similar data format from different sources results in data loss irrespective of the quality or value of the data in similar formats.
Data Lake can be used when an organization deals with quickly changing raw, structured, or semi-structured data and when managed by data scientists. Data Warehouse can be used when an organization deals with slowly changing data and requires daily, weekly, and monthly summaries of known structured data and when used by business and operational users. Sometimes, organizations use combinations of both, like storing the data in Data Lake and moving the richer data to the data warehouse for advanced reporting.