In the age of big data, understanding databases and warehousing is a necessity. Various organisations are rapidly producing a vast amount of structured and unstructured data. It is harnessed to discover prospects and opportunities for getting better in terms of performance, efficiency, revenues, or anything that it resonates with. These results can be achieved, provided the data storage and handling are strong. For this purpose, data lakes and data warehousing are popularly used. These are two unique terms that help in transforming data, encompassing data entry to cleansing and then processing for machine learning and AI algorithms.
In this post, you’re going to discover these two terms and how they are unique and purposeful for businesses.
What is a Data Lake?
This is a centralised repository that provides virtual space to organisations for structured and semi-structured data storage. Unlike traditional databases like hard discs, these lakes allow to secure a tonne of data in different formats, as in text, images/PDFs, audio, and video. Organisations appreciate this flexibility, storing raw or crude data without the need for preprocessing. Simply put, it is a storage for data ingestion, which leads to data transformation, preprocessing, and processing.
Key Features of Data Lakes
1. Schema-on-Read
Data lakes follow schemas. These are mostly schema-on-read, which enables structuring of data only when it is used for analysis. So the data specialist can leverage its greater agility to define schemas according to the analysis he or she wants to carry out.
2. Scalability
Data lakes are majorly designed on distributed storage, which allows to scale them horizontally. In all, these lakes have the capacity to handle a massive volume of data from multiple sources. And this can be done without experiencing significant degradation in their performance.
3. Cost-Effectiveness
Since these lakes are designed to store data in its raw form, it can be less expensive than transforming it into structured formats. Overall, it can be an economical solution for businesses that experience massive data flow for data scientists.
4. Usefulness
Lakes typically allow data in its raw form to get ingested. Being occupied with multiple formats, this storage may run queries slowly, especially when the data is complex. But for machine learning and deep analytics, this is amazingly useful.
Popular Data Lake Technologies
- Apache Hadoop
Hadoop is an open-source framework that makes distributed storage smooth and convenient for further processing of large databases.
This is a cloud-based service, which businesses use to keep their data and retrieve any amount of records from anywhere.
This is scalable data storage, which smooths the process of data analytics.
Advantages of Data Lakes
1. Flexibility
A wide range of data types and formats can be effectively managed in these lakes. This feature makes it the best fit for diverse analytical needs.
2. Scalability
Data lakes can be effortlessly scaled up or down if data volumes increase or decrease over time. And for this purpose, the facility does not need reconfiguration.
3. Cost-Effective Storage
Storing data can have credible details. So, it is precious. Besides, it is easy-to-afford, especially when it comes to dealing with large datasets.
Disadvantages of Data Lakes
1. Complexity in Management
The unstructured nature of data lakes can be troublesome. Managing and retrieving data from such complex and variable data formats will be challenging.
2. Data Quality Concerns
It allows data ingestion without preprocessing. So, the issues in data quality and governance will be commonly observed to implement best practices for quality improvement.
3. Performance Issues
Running queries of large volumes of unstructured data won’t be smooth. It will turn the whole querying system sluggish.
What is a Data Warehouse?
A data warehouse is a centralised data storage. Unlike data lakes, it is specifically designed to accommodate space for well-defined and structured data analysis and reporting. It combines data from multiple sources at a place, which allows logical data modelling to take place. Overall, integrating business intelligence tools with it is easy for meaningful insights.
The only point where it differs from data lakes is that it allows clean, transformed, and organised data to be stored in the system.
Key Characteristics of Data Warehouses
1. Schema-on-Write
It uses a schema-on-write, which means designing a schema beforehand to structure and analyse data. The user has to simultaneously maintain consistent formatting and optimisation of data to comply with structured query language.
2. Performance Optimisation
With optimised and systematic data in warehouses, the query will run fast. It proves outstanding for complex analytical workloads. Two things, indexing and distribution strategies, facilitate laser-fast querying.
3. Data Governance
It is well known that warehouses accept structured data. Typically, it represents that the processed data is sensitive and highly insightful. So, data warehouses typically follow more robust data governance and security measures so that the quality of data will be high. Also, its compliance with regulations mitigates the risk of going against compliance.
4. Business Intelligence
Being occupied with structured and insightful data, data scientists or analysts use it to drive business insight-driven strategies or plans.
Popular Data Warehouse Technologies
- Amazon Redshift
This is actually a cloud-based data storage and processing service, where fast query performance with SQL tools is normally observed.
- Google BigQuery
Unlike others, this is not supported by any server. But still, the data can be handled with care. And fast SQL queries can be run using Google infrastructure.
- Snowflake
This is equipped with cloud facilities, where users can manage, process, and analyse both structured and semi-structured data.
Advantages of Data Warehouses
1. Optimised for Analysis
Data warehouses are specifically designed to enable fast querying and reporting. Driving business intelligence is easy with them.
2. Data Quality Assurance
Before ingestion, data passes through a rigorous funnel of cleaning and data transformation services. This facility ensures higher quality.
3. User-Friendly
The structured nature of data warehouses makes it easier for users to generate reports and insights.
Disadvantages of Data Warehouse
1. Higher Costs
It is pricey to afford a data warehouse. Its ability to store transformed data and structured storage require higher costs compared to data lakes.
2. Inflexibility
Warehouses are less scalable when it comes to storing new data types and structures.
3. Longer Preparation Times
The ETL process, or Extract, Transform, Load, is lengthy, which leads to delayed analysis.
When to Use Data Lakes vs. Data Warehouses
Prefer either a data lake or a warehouse—it’s a big question, which can be resolved by understanding specific needs.
- You may invest in data lakes when
- The need is to occupy gigantic unstructured or semi-structured data.
- Your requirement is to get into insights and extract machine learning models.
- You require agility and flexibility to accomplish data processing and in-depth analysis.
2. You may need data warehouses when
- A virtual space is required for fast and reliable insights and reporting.
- The data is structured and should be stringently guarded.
- Regular querying support is required for dashboard updates.
Conclusion
Data lakes and warehouses are advanced data storage that are evolved to meet the progressive needs of data analytics, business intelligence, machine learning modelling, and more. However, both are helpful. But each one has its own upsides and downsides. Where the data lake is ideally to occupy unstructured data before ingestion, the warehouse allows structured or semi-structured data to be stored. Likewise, some other differences are mentioned above. You can choose the one according to your business requirements.