What are the most commonly used data storage platforms for data validation?

Data validation is the process of checking the accuracy, completeness, and consistency of data before using it for analysis or reporting. Data validation is essential for ensuring data quality and avoiding errors, anomalies, or inconsistencies that could affect the results or decisions based on the data. Data validation can be performed at different stages of the data lifecycle, such as data ingestion, data transformation, data loading, or data querying. Depending on the type, source, and volume of data, different data storage platforms can be used for data validation. In this article, we will discuss some of the most commonly used data storage platforms for data validation and their advantages and disadvantages.

1 Relational databases

Relational databases are one of the most widely used data storage platforms for data validation. Relational databases store data in tables that have predefined schemas, constraints, and relationships. This allows for enforcing data integrity, consistency, and quality rules at the database level. Relational databases also support SQL, a standard query language that can be used for data validation queries, such as checking for null values, duplicates, outliers, or foreign key violations. Some of the advantages of relational databases for data validation are their scalability, performance, security, and compatibility with various tools and applications. However, some of the disadvantages are their rigid schemas, high cost, and limited support for unstructured or semi-structured data.

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Anandu S.

Cloud Architect Intern @ Illumio | MSSE Grad @SJSU | Ex- Deloitte
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Common relational databases like MySQL, PostgreSQL, and Oracle are used for data validation. MySQL is open-source with robust performance and supports various data types. PostgreSQL is known for standards compliance and extensibility, offering data integrity features. Oracle is commercial, praised for advanced features, scalability, and reliability. Data validation often involves table-level constraints like `NOT NULL`, `UNIQUE`, and `CHECK` to maintain data accuracy and consistency. However, relational databases may not suit all use cases.

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2 Data lakes

Data lakes are another popular data storage platform for data validation. Data lakes are repositories that store large volumes of raw, unstructured, or semi-structured data in their original format. Data lakes allow for storing diverse types of data, such as text, images, videos, audio, or JSON files, without imposing any schema or structure. This enables data validation to be performed on demand, using various tools and frameworks, such as Spark, Hadoop, or Python. Some of the advantages of data lakes for data validation are their flexibility, scalability, low cost, and support for multiple data sources and formats. However, some of the disadvantages are their lack of governance, security, and metadata management, which can lead to data quality issues, such as data silos, data duplication, or data inconsistency.

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Anandu S.

Cloud Architect Intern @ Illumio | MSSE Grad @SJSU | Ex- Deloitte
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Amazon S3, a popular data lake, can store diverse raw data. For data validation, tools like Apache Spark can be used. For example, a media company might store video footage, scripts, and viewer feedback in S3 and use Spark to validate this data. While the flexibility and scalability of data lakes are advantageous, they require robust data management practices to avoid issues like data silos, duplication, or inconsistency.

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3 Data warehouses

Data warehouses are another common data storage platform for data validation. Data warehouses are centralized repositories that store structured, processed, and aggregated data for analytical purposes. Data warehouses typically follow a dimensional model, such as star or snowflake schema, that organizes data into facts and dimensions. Data warehouses also support SQL and other analytical tools and languages, such as R or SAS, for data validation. Some of the advantages of data warehouses for data validation are their high performance, reliability, and consistency, as well as their support for complex queries and calculations. However, some of the disadvantages are their high cost, complexity, and maintenance, as well as their limited support for unstructured or semi-structured data.

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4 NoSQL databases

NoSQL databases are another alternative data storage platform for data validation. NoSQL databases are non-relational databases that store data in various formats, such as key-value pairs, documents, graphs, or columns. NoSQL databases do not have fixed schemas or constraints, which allows for storing and validating dynamic, heterogeneous, and distributed data. NoSQL databases also support various query languages and APIs, such as MongoDB, Cassandra, or Neo4j, for data validation. Some of the advantages of NoSQL databases for data validation are their flexibility, scalability, and performance, as well as their support for unstructured or semi-structured data. However, some of the disadvantages are their lack of standardization, security, and consistency, as well as their difficulty in handling complex queries and calculations.

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5 Cloud storage

Cloud storage is another emerging data storage platform for data validation. Cloud storage is a service that provides remote access to data stored in the cloud, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage. Cloud storage allows for storing and validating large amounts of data, without requiring any physical infrastructure or maintenance. Cloud storage also supports various tools and services, such as AWS Glue, Google Dataflow, or Azure Data Factory, for data validation. Some of the advantages of cloud storage for data validation are its low cost, scalability, and availability, as well as its integration with various cloud-based platforms and applications. However, some of the disadvantages are its security, privacy, and compliance risks, as well as its dependency on network connectivity and bandwidth.

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Asheesh ..

Lead Data Engineer | Azure Data Trainer 🌳 | Microsoft Student Partner | Data Engineering | Author | AWS and Azure Certified | Helped 200+ Candidates in switching to Data Engineering |
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Cloud Data storage are nowadays in so much demand because of multiple reasons - 1. These storages like AWS S3 or Azure blob are easier to use and manage. 2. We can create some python script and schedule to validate the data available inside these storage using cloud function or AWS Lambda. 3. Various security services are available on these cloud storage to make it secure and authorised only access.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most commonly used data storage platforms for data validation?

1

2

3

4

5

6

1 Relational databases

2 Data lakes

3 Data warehouses

4 NoSQL databases

5 Cloud storage

6 Here’s what else to consider

Data Engineering

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What are the most commonly used data storage platforms for data validation?

1

2

3

4

5

6

1 Relational databases

2 Data lakes

3 Data warehouses

4 NoSQL databases

5 Cloud storage

6 Here’s what else to consider

Data Engineering

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