Two Data Storage Options for Your Organization to Consider
The simple fact is that the amount of data an organization collects is vast. It is predicted by 2025 the volume of big data collected worldwide will be 2x on 2020 levels. This is driven by an increasingly digital world and data systems designed to collect data.
As well as how the data is collected and accessed and processed, one thing that raises its ugly head is how are you going to store this data.
Two options are data lake vs data warehouse. But what are the differences and what one is best for your organization? Read on to find out.
Data Lake
Data lakes can store large amounts of structured, unstructured, and semi-structured data. In fact, any kind of data in its native form can be stored in a data lake with no fixed limits on account size or file. It is basically a lake full of data, hence the name.
Data flows into the lake in real-time, and analytical performance and native integration can be utilized.
Data Warehouse
Whereas data lakes bring all the data together in whatever form it is stored, data warehouses are more like a filing system. It provides a multidimensional view of the summary an atomic data and is geared towards:
- Data extraction and cleaning
- Data transformation
- Data loading and refreshing
With this in mind, let’s look at the differences between the two.
Data Lake vs. Data Warehouse the Differences
- Data is stored in a data lake irrespective of the source, while Data warehouses stores data in quantitative metrics with relevant attributes.
- Data Lakes store the data as a river whereas Data Warehouses allow for a more strategic storage approach.
- Schema is created after data is stored in a data lake. Conversely, Schema is created before any data is stored.
- Data Lake utilises ELT (Extract Load Transformation) while data warehouses utilize ETL (Extract Transform Load).
Generally, data lakes are good for in-depth analysis whereas warehouses are better for operational users.
As you can see deciding on one solution is almost impossible which is why many organizations use a hybrid of both data lakes and data warehouses.
Utilizing the Data Lake and Data Warehouse Hybrid
Generally using a hybrid system improves data visibility, governance, and security, together with lowing costs. It also:
- Process data rapidly – With the hybrid system operational you can utilise batch processing and schema-on-read use can use the data lake to load data for analysis faster into the lake than you can the warehouse.
- Enhanced querying – Enhanced or federated querying lets you retrieve relational and non-relational data in a single retrieval query, negating the need for different tools. This is a real-time saver and productivity boost.
- Low costs – Data lakes offer cost advantages over data storage premises. This takes the form of no premises and server rent, together with lower data transformation costs.
- Enhanced compliance, enhanced security – A clever aspect of the hybrid system is that sensitive data can bypass the data lake and go straight into the data warehouse. Better visibility of data is another security and compliance factor that is boosted by the hybrid system.
Both data warehouses and data lakes have strengths and weaknesses. The great thing about the hybrid system is that you get the best of both worlds. Consider it for your business.
