The State of Data Engineering: A Look at the Latest Trends

About Author : Manu Mathew, Client Partner – Analytics @ BigTapp Analytics 

Data engineers are responsible for building and maintaining the infrastructure that enables data scientists to do their work. This includes tasks such as

Designing and implementing data pipelines,

1. Selecting and implementing the right data storage and processing technologies

2. Ensuring the quality and security of the data.

Data engineering is a rapidly growing field, with new technologies and techniques being developed all the time.

 

If you’re interested in pursuing a career in data engineering, you’ll need to have a strong foundation in computer science, statistics, and mathematics. You’ll also need to be comfortable working with large datasets and complex systems, and be able to adapt to new technologies and challenges as they arise.

 

Overall, data engineering is a dynamic and exciting field that offers numerous opportunities for those with the right skills and expertise. Whether you’re just starting out or are an experienced data professional, there are many exciting challenges and opportunities in the field of data engineering.

 

In this article, I want to explore some of the trends we are seeing in Data Engineering. While in the earlier years, the increasing volume, velocity, and variety of data being generated by businesses and organizations, drove the need for data engineering to handle this data effectively, the industry is now maturing to handle the intricasies that come with the process.

 

Trend #1 : The shift from ETL to ELT.

ETL (Extract, Transform, Load) is a traditional data integration approach that involves extracting data from multiple sources, transforming it into a consistent format, and then loading it into a target data warehouse or data mart. ETL has been widely used for many years, but it has some limitations.

 

One of the main limitations of ETL is that it can be slow and inflexible, especially when dealing with large volumes of data. The transformation phase of the ETL process can be particularly time-consuming, as it involves applying complex rules and algorithms to the data to clean and standardize it. This can make it difficult to quickly and easily update or modify the data pipeline to meet changing business needs.

ELT (Extract, Load, Transform) is a newer data integration approach that seeks to overcome these limitations. In ELT, the data is first extracted and loaded into a target data store, such as a data lake or cloud data warehouse. Then, the data is transformed using SQL or other tools, allowing for more flexibility and faster processing.

 

There are several key benefits to using ELT instead of ETL. First, because the data is loaded into the target data store before it is transformed, ELT allows for much larger volumes of data to be processed. This can be particularly useful for organizations dealing with big data or real-time data streams.

 

Second, ELT allows for more flexibility and faster iteration. Because the transformation phase is decoupled from the data loading phase, it is easier to update or modify the transformation logic without having to re-run the entire ETL process. This allows for faster testing and experimentation, and makes it easier to adapt to changing business needs.

 

Overall, the move from ETL to ELT represents a shift towards more flexible and scalable data integration approaches. By decoupling the data loading and transformation phases, ELT allows for faster and more flexible processing of large volumes of data. This makes it a valuable tool for organizations looking to unlock the value of their data and drive better business outcomes.

 

One of the key benefits of ELT is the ability to take advantage of the scalability and flexibility of cloud data warehouses. With traditional on-premises data warehouses, it can be difficult and expensive to add more storage or processing power as the amount of data grows. In the cloud, however, data engineers can simply add more capacity as needed, without having to worry about hardware or software constraints.

Furthermore, many cloud data warehouses offer built-in support for ELT processes, with tools and libraries for extracting, loading, and transforming data. This makes it easier for data engineers to implement ELT pipelines, and allows for faster and more flexible data integration.

 

Overall, the rise of cloud data warehouses has driven the adoption of ELT as a data integration

 

Trend #2: The rise of cloud-based data engineering.

The rise of cloud computing has had a major impact on the field of data engineering. In the past, data engineers were responsible for building and maintaining complex on-premises data infrastructure, including hardware, software, and storage systems. This was often a time-consuming and expensive process, and it limited the scalability and flexibility of the data pipeline.

With the advent of cloud computing, data engineers now have access to a wide range of cloud-based data storage and processing solutions. These solutions are typically much easier to set up and manage than on-premises systems, and they offer greater scalability and flexibility.

 

One of the key benefits of cloud-based data engineering is the ability to easily and quickly scale up or down as needed. With on-premises data infrastructure, it can be difficult and expensive to add more storage or processing power as the amount of data grows. In the cloud, however, data engineers can simply add more capacity as needed, without having to worry about hardware or software constraints.

Another benefit of cloud-based data engineering is the ability to take advantage of advanced data processing and analytics tools. Many cloud providers offer built-in support for technologies such as Hadoop, Spark, and Flink, which can be used to process and analyze large volumes of data. This makes it easier for data engineers to build and maintain complex data pipelines without having to invest in expensive on-premises infrastructure.

 

Overall, the rise of cloud-based data engineering has made it easier and more cost-effective for organizations to store, process, and analyze large volumes of data. By leveraging the power of the cloud, data engineers can build flexible and scalable data pipelines that can support the evolving needs of their business.

 

There are many different tools and technologies that data engineers can use to support cloud-based data engineering. Some of the most popular options include the following:

1.   Cloud data warehouses: These are specialized databases designed for storing and querying large volumes of structured data. Examples include Amazon Redshift, Google BigQuery, and Snowflake.

2.   Cloud data lakes: These are repositories for storing large amounts of structured and unstructured data, and are typically used for data analytics and machine learning applications. Examples include Amazon S3, Azure Data Lake, and Google Cloud Storage.

3.  Cloud Lakehouses: A lakehouse combines the scalable storage of a data lake with the structured query capabilities of a data warehouse, allowing for flexible and efficient data integration. This is driven by companies like Databricks and other open source storage technologies that support data warehouse like operations on the data hosted within the data lake.

4.   Cloud data integration and ETL tools: These tools make it easier to extract, transform, and load data from multiple sources into a target data store. Examples include AWS Glue, Azure Data Factory, and Google Cloud Dataprep.

5.  Cloud-based analytics and visualization tools: These tools allow data engineers to analyze and visualize data stored in the cloud, and can be used to create dashboards, reports, and other outputs. Examples include Amazon QuickSight, Google Data Studio, and Tableau.

Trend #3: The growing focus on data quality and governance. 

As organizations collect and analyze larger and larger volumes of data, it is critical to ensure that the data is accurate, consistent, and secure.

 

Data engineers play a critical role in ensuring data quality and governance. They are responsible for designing and implementing data pipelines that can handle large volumes of data, and for ensuring that the data is cleaned, standardized, and secured.

 

To support data quality and governance, data engineers are using a range of tools and technologies. These include data catalogs and metadata management systems, which help to document and manage the data pipeline, and data governance frameworks, which provide guidelines and best practices for ensuring data quality and security.

 

Another important aspect of data quality and governance is the role of data governance committees and teams. These groups are responsible for defining and enforcing data policies and standards, and for monitoring and enforcing compliance with these policies.

 

Overall, the growing focus on data quality and governance reflects the increasing importance of data in driving business insights and decision-making. By working closely with data governance teams and using the right tools and technologies, data engineers can help ensure that data is of high quality and is used in a responsible and ethical manner.

Conclusion

In conclusion, there are several key trends in the field of data engineering that are driving the development of new technologies and approaches to data management and analysis. These trends include the shift from ETL to ELT, the rise of cloud-based data engineering, and the growing focus on data quality and governance.

 

The shift from ETL to ELT represents a move towards more flexible and scalable data integration approaches. By decoupling the data loading and transformation phases, ELT allows for faster and more flexible processing of large volumes of data.

 

The rise of cloud-based data engineering has made it easier and more cost-effective for organizations to store, process, and analyze large volumes of data. By leveraging the power of the cloud, data engineers can build flexible and scalable data pipelines that can support the evolving needs of their business.

 

Finally, the growing focus on data quality and governance reflects the increasing importance of data in driving business insights and decision-making. By working closely with data governance teams and using the right tools and technologies, data engineers can help ensure that data is of high quality and is used in a responsible and ethical manner.

 

A Look at the Latest Trends : Authored by Manu Mathew, Client Partner – Analytics

 https://medium.com/@manugmathew/the-state-of-data-engineering-a-look-at-the-latest-trends-ec3198eefb37