You're about to test a data lake. How do you know it's ready for production?

In my view, some steps should be considered so that it can be taken to the Production Environment, such as: Check the accuracy, integrity and consistency of the data stored in the data lake, Ensure that data access controls and encryption mechanisms are in place. in place to protect sensitive information, Perform performance tests to evaluate the speed and scalability of the data lake, Validate the metadata in the data lake, including schema information, data lineage, and documentation, Test the integration of the data lake with other systems, applications and tools, Verify the effectiveness of backup and recovery processes, Ensure data governance policies and procedures are implemented.

Data quality is crucial for accurate analysis and reporting. We ensure accuracy, completeness, consistency, and reliability of data in the data lake. Tools like Apache Spark and Apache Beam help us validate schemas, profile data, clean, and remove duplicates to maintain data quality.

Initiating data quality checks by comparing the integrity of data with source systems is a solid approach. Integrating basic data quality checks, including duplicate checks, error record checks, null checks, within the ETL pipeline itself is beneficial. In cases where variances between the source and the new data lake system are anticipated, collaboration with business teams becomes crucial. Both teams should agree upon a threshold for acceptable variances, and it is crucial to ensure that the data in the data lake remains within this agreed-upon threshold. This collaborative approach helps maintain data integrity and quality throughout the process.

The key element of a Lake House is the decoupling of storage and compute, meaning that any authorized application can read any data. This can impact on the data quality, as the final users can be reading bad data. Is a good practice to have to have different layers (Broze, Silver and Gold). Final users will read from Gold layer, meanwhile we keep the "historical" data in the other layers for Machine Learning or other purposes.

To test a Data Lake readiness for production,the following points of testing are crucial: 1. Data Source validation & linkage: Identification of the actual source system is crucial (SAP, CAR, SAP SD, EWM, ECC, etc).This is the basis of DataLake implementation.One needs to verify if the source systems have been identified correctly. 2. SLT replication: Verify if the data is being replicated correctly into the DataLake landing layer. 3. Data sanctity:Verify for the correctness of data journey from landing,to staging,to consumption layer. 4. Logic testing: Confirm if the calculation logic implemented are correctly understood and implemented. 5. Access Visibility: Data access should be restricted to required personnel only.

The key benefit of the Lake House is the openness and support for any workload. Having the data in an Object Storage (like S3) allows that any supported tool (BI, Database, Big Data) to read and analyze this data. This openness saves a lot of time in ETL meanwhile achieving more data flexibility.

Data Accessibility: Data accessibility refers to how easily users and applications can access, query, and analyze data within the data lake. This involves having a clear and documented data catalog, metadata, and schema detailing data sources, formats, and meanings. Testing query performance, scalability, and availability across various workloads and scenarios is essential. Tools like Apache Hive, Presto, or Dremio can facilitate SQL-based access and optimize queries in the data lake.

Testing a data lake for production readiness generally involves following steps: - Ensure data quality by validating completeness, accuracy, consistency. - Verify that appropriate data governance policies and access controls are in place to protect sensitive data and comply with regulations. - Assess the security measures implemented to protect data from unauthorized access, and other security threats. - Test data integration processes to ensure seamless data ingestion and transformation from various sources. - Ensure compliance with relevant industry standards and regulations, such as GDPR, HIPAA. - Conduct UAT with end-users to validate that the data lake meets their requirements and expectations.

Indeed, implementing robust access control mechanisms is vital. Categorizing data according to its sensitivity and granting access solely to the relevant audience is essential. Prior to releasing the data lake to users, it is essential to conduct thorough testing to verify and validate these access controls, ensuring that only the intended teams has the necessary permissions. Furthermore, in addition to access restrictions, incorporating encryption or data masking based on specific use cases can contribute an additional layer of security.

A great power comes great responsibility. Could be valid for Superman and for Lake Houses architectures ?? Nowadays cloud services provides great capabilities for managing the security. However, it is also very important to combine this security with the consuming layers. For example if we have a database reading data from lake houses, it is very important to validate the security from this database and improve it if required (data masking, data access based in application users, etc)

Once you start storing data in a lake house, if you don't govern it, it could become a data swamp. Storing data doesn't mean it could be useful in the future. Data Catalogs are a key components to keep track of all the data, all the changes and where is it stored if you are working with many consumer repositories like databases.

A key area of making your data lake ready for production is data governance, you need to make sure the data complies with your business objectives and regulations. Without a proper data governance and also data lineage foundations, you could leak PII data, you could duplicate important business metrics and easily lose trust from your data consumers. Even if you can get something quick in production you need this structured first to avoid the aforementioned problems.

Normally, customers uses the Lake House architecture as an extension of the Data Warehouse. The first question to answer is, why I need it and why the Data Warehouse is not able to meet my requirements? For a simple scenario, could complex the data architecture and lose flexibility.

Un lago de datos también debe tener asociado los siguientes puntos para estar en produción: 1) Plan de recuperación de datos: por si en algún momento los datos son corrompidos o aislados de uso. 2) Documentación de architectura de almacenamiento: saber como organizar los datos para que el almacenamiento evolucione. 3) Herramienta de gestión de metadatos asociados: saber información de los objetos más complejos. 4) Organización estructurada sobre permisos sobre los recursos, objetos y secciones a trabajar.