We experience the great impact of data both on our lives and business. Huge amounts of information if used correctly can be a key to success. But those great amounts of data must be stored and analyzed in an effective way.
In this article, we’ll highlight the role of data for modern businesses and explain key differences between data warehouses and data lakes.
The role of data in business
Data storage and access are the core concepts behind any data-driven business. It is a crucial part of an organization as the data stored is a valuable asset. Data collected and stored over the years can provide valuable insights if mined and translated appropriately. Analysis of data can help predict the future course of action for the business.
In times gone by, companies used traditional databases to store a limited quantity of data. However, for predictive analysis, massive amounts of data are required to obtain conclusive results.
To do this, companies use the concept of Big Data. Big Data indicates enormous datasets containing a great variety of structured and unstructured data for predictive analysis. It is impossible to store Big Data in traditional databases.
Data warehouse and data lake: definitions
A data warehouse or enterprise data warehouse (EDW) is an ample storage space that can hold data from various sources within the organization.
The data stored here is structured, current, and historical data. This data is used for mining information for analysis to help the management make crucial business decisions.
Examples of data warehouse software: Amazon Redshift, Snowflake, IBM Db2. Probably, the most popular data warehouse is Google BigQuery.
It’s widely used by companies of different sizes. It’s an integration-friendly solution that allows you to keep track of all the important business records. For example, having one of BigQuery integrations significantly empowers your corporate data analytics.
On the other hand, a data lake is also a central repository that stores both structured and unstructured data for easy access at any time. The data stored in a data lake is usually in its raw or native format. Organizations implement data lakes on cloud-based storage platforms to make them highly scalable.
Examples of data lake software: Azure Data Lake Storage, Amazon S3, Google Cloud Storage.
The main difference between a data lake and a data warehouse is the nature of the stored data. Data lake consists of vast numbers of raw, unstructured, and undefined data. In contrast, a data warehouse consists of massive structured and filtered data usually pre-processed for a specified purpose.
7 differences between data lakes and data warehouses
Significant differences include:
1. Storage of data
The data stored in a data warehouse is highly structured and processed, which has been filtered through specific criteria. The data which does not conform to the requirements is discarded or not considered eligible for storage in the warehouse.
In a data lake, however, all data types are deemed suitable for storage, and no filtering criteria are applied. A data lake stores all data in its raw format. This format makes it easier and faster to upload data into a data lake.
Data upload in a data warehouse follows a defined ETL (Extract, Transform, Load) process for filtering. Data lakes can store an unlimited amount of data without increasing costs hugely. Scaling up and down is certainly more convenient in Data lakes.
2. Flexibility of data access
Access to data stored in data lakes is notably more uncomplicated and quicker. The raw data storage format in data lakes makes it flexible in analyzing data for business intelligence within the framework of security and privacy defined by the organization. Businesses have a limited purview of the stored data in a warehouse. Since the data in the warehouse is stored in a specific format and filtered to hold only a particular type of data, the analysis options are limited.
3. Data processing on reading access
In a data warehouse, the data modeling is performed upfront while uploading the data. In this case, the database schema is decided and structured during the writing process, known as schema-on-write. For new requirements, the schemas are altered and data transformed as per the new schema.
In comparison, in data lakes, the data is uploaded irrespective of its data type and loaded in its raw format. The data formatting is done when data is retrieved for processing; this method of data modeling is known as schema-on-read. This type of data modeling provides flexibility in how the data is treated on the basis of each use case. The schema can be discarded if it is of no further use.
Also, a new schema can be developed to read the data as per the new requirement.
4. Data storage type supports all users
Data lakes make it easier for all users to store their data. In a data warehouse, structured data requires a predefined schema that is rigid and binding. This structured schema and data are useful for business users and decision-makers who prefer reports in a specific format to help them make critical business decisions.
However, the unstructured data format of data lakes allows flexibility of usage by anyone across the organization. For example, data scientists can pick raw data for analysis and prediction. The pre-processed data in the data warehouse may be insufficient for use by a data scientist.
5. Adaptability to changes
Data lakes are structured to adapt quickly to changes. In a data warehouse, the data is processed upfront and loaded. Time and effort are spent in building the database and the schema. It takes a reasonably long time for a data warehouse to adapt to new changes.
In data lakes, since the schema is required only during reading, it is easier to make changes to the data structure at any time. Data reload and upload time is minimal in comparison to that of a Data warehouse.
6. Data security
The structured manner of data storage in the data warehouse ensures tight data security. It is easy to manipulate the data structure in data lakes as they do not follow a specific format. The metadata usually resides along with the data, making it vulnerable and susceptible to security threats.
Data lakes are not desirable where data security is of utmost concern, as in the financial and banking sectors. Data security is still immature and evolving since data lakes are a relatively new concept.
7. Storage and compute decoupled
Cost optimization is possible in data lakes since storage is independent of computing. Storage costs can be optimized as per requirement and frequency of access. The decoupling also makes it easy to archive raw data to low-cost tiers while providing faster access to transformed and process-ready data.
In a data warehouse, the storage and computation are tightly coupled. Increasing storage automatically increases computing as well.
Advantages of data warehouses over data lakes
Data lakes have a broad customer base due to their flexible properties. However, data lakes do not follow database best practices, where a data warehouse takes the upper hand.
Here are some of the cases where a data lake is not the preferred choice.
1. Vast and redundant data
Since data lakes allow all data to be stored without any filtering, there is a tendency to accumulate all the data in the data lake. Due to a lack of sanity checks, even unwanted, repeated, and irrelevant data gets stored, which may not be of any value to data scientists. It becomes a tedious exercise to sort the data later. A data warehouse, on the other hand, follows all the rules of database storage during its ETL process.
2. Lack of data prioritization
Since there are no data checks and all types of data can be stored without hassles, there is no or very little importance given to prioritizing data. This lack of prioritization results in increased data storage costs.
3. Increased data latency
Data stored in data lakes are primarily used for more extensive analytics and reporting purposes. Suppose the interactive response time on the queries display a lag. In that case, the analysis process will be affected, which would not be appropriate for the company.
4. Risk of violating rules of compliance and regulations
As there are no checks done before uploading data to the data lakes, most often, there is a risk of running into compliance issues in terms of the sources of data. Also, essential and statutory information cannot be stored directly in the database, leaving it vulnerable to any security threats.
Conclusion
Data is valuable and beneficial when stored in a systematic and meaningful manner. Storage of data calls for serious thinking on the most appropriate storage medium. Think through the pros and cons of data lakes and data warehouses before choosing the best one for your needs.
