What are the most useful SQL and NoSQL functions and features that data engineers should master?

1 SQL functions

SQL, or Structured Query Language, is the standard language for interacting with relational databases which store data in tables with rows and columns. SQL functions are predefined operations that enable calculations, transformations, or aggregations on data. Data engineers can take advantage of various SQL functions to perform statistical analysis, assign values or ranks to rows, manipulate text data, work with date and time data, and apply logic to their queries. These include aggregate functions such as SUM, COUNT, AVG, MIN, MAX and GROUP BY; window functions such as ROW_NUMBER, RANK, DENSE_RANK and PARTITION BY; string functions like CONCAT, SUBSTRING, REPLACE and TRIM; date and time functions like DATE, TIME, DATETIME, NOW and INTERVAL; and conditional functions like CASE, IF and COALESCE.

2 NoSQL features

NoSQL, or Not Only SQL, is a term used to refer to a wide range of non-relational databases that store data in various formats, such as documents, key-value pairs, graphs, or columns. Data engineers can benefit greatly from NoSQL features, such as schema flexibility which allows for data storage and querying with different attributes or fields. Additionally, scalability allows for the handling of large volumes of data distributed across multiple servers or nodes without compromising performance or availability. Performance is also improved, particularly for read-intensive or write-intensive operations and complex queries involving multiple joins or aggregations in SQL databases. Lastly, variety supports different types of data and offers different query languages or APIs for interaction.

3 SQL and NoSQL comparison

When choosing the right database for a data engineering project, several factors must be taken into consideration, such as the nature, volume, and velocity of your data, the complexity and frequency of your queries, and the trade-offs between consistency, availability, and partition tolerance. Generally speaking, SQL databases should be used if your data is structured, consistent, and relational, and if you need to perform complex analytical queries while ensuring data integrity and accuracy. On the other hand, NoSQL databases are better suited for unstructured, heterogeneous, and non-relational data that requires large amounts of storage and processing power. Furthermore, NoSQL databases offer scalability and flexibility that SQL databases do not.

4 SQL and NoSQL integration

In some cases, you may need to use both SQL and NoSQL databases in your data engineering project to benefit from the advantages of each and address different data requirements. For instance, you can utilize a NoSQL database to store raw or streaming data from various sources, and a SQL database to store processed or aggregated data for reporting or analysis. To integrate these databases, you can employ various tools or techniques such as ETL (extract, transform, load) pipelines, data lakes, and polyglot persistence. ETL pipelines are processes that extract data from one or more sources, transform it into a desired format or structure, and load it into a target destination. Data lakes are centralized repositories that store raw or unprocessed data in its original format and allow you to access and analyze it using different tools or methods. Polyglot persistence is a strategy that uses multiple types of databases to store different types of data based on their characteristics and needs.

5 SQL and NoSQL best practices

In order to maximize the use of SQL and NoSQL databases in your data engineering project, it is important to understand your data and use case, and choose the appropriate database type and model. Additionally, designing a database schema or structure according to query patterns and performance expectations is essential. To ensure optimal performance and reliability, testing and monitoring your database is recommended. Security should also be a priority; using encryption, authentication, authorization, and backup methods can help protect your data from unauthorized access or manipulation.

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?