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Powering Data Engineering Through Automation

 

Data engineering as a discipline began over two decades ago, but did not initially receive much attention. It was hidden inside of business intelligence (BI), data integration, data management, and IT operations. Today, with the popularity of machine learning in every industry, we have seen a surge of demand for data engineering support for data science teams, and as a result, many organizations are now beginning to develop their data engineering expertise. This supports the data science team, and also  their wider data and analytics initiatives. In simple terms, the data engineering team in essence translates data into forms readily consumable to data scientists, data analysts and business users.

 What are the benefits of data engineering?

Organizations that invest in data engineering will be able to make better decisions, provide more comprehensive customer experiences, and help identify new business opportunities through:

  • Faster time-to-delivery when adding new data sources to existing analytics and data science models.
  • Faster decision-making for business teams, since they have access to ready-to-use data.
  • The ability to incorporate third-party data more quickly.
  • The ability to meet regulatory and compliance requirements with ease.

What is data engineering?

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. For example, a data engineer can reorganize data into usable forms, such as data and analyst applications. A data engineer can extract data from an application via an API, transform that data, and load it into a data store to enhance business decision making. However, a data engineer’s job is not easy and they are faced with new challenges more frequently than often.

Challenges Faced by Data Engineers

Data engineers face various challenges in different stages of the data engineering process. Some of challenges they may face are:

Data Ingestion and Processing: Data engineers often spend a lot of time ingesting and processing data from different sources. However, data ingestion and processing can be challenging when dealing with large volumes of data. Data engineers need to ensure that the data ingestion process is reliable and scalable. Furthermore, different data formats, structures, and speed exemplifies these challenges when they are ingesting data from various sources. They need a tool that can automatically process and transform data from a wide range of sources – including niche and custom in-house applications.

Data Cleansing and Validation: Data cleansing and validation are crucial steps in the data engineering process, as data quality is essential for accurate analysis and decision-making. (NOTE: Your analysis is as good as your data). Hence, the need to identify and manage inconsistent, incomplete, duplicate or missing data. To be efficient, they also need to ensure that data validation rules are in place to prevent data entry errors and ensure that data is accurate and consistent. Ideally, an automation engine can help them by automatically identifying and fixing data quality issues.

ETL and ELT: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are the processes used to transform and load data from source systems to the target data warehouse. Data engineers need to design and develop efficient ETL/ELT pipelines. They also need to ensure that pipelines are reliable, scalable, can handle peak loads and errors, and can be monitored.This can easily get complex as they have to deal with various data sources, types and speeds. Ideally, they would want to off-load some of these tasks for the business users as they have more insights into the business and can help them reduce the back and forth, which is very time-consuming, with the business users.

Data Monitoring: Data Engineers have to deal with multiple tasks and can’t keep up with one of requests from business users. Monitoring and controlling pipelines might not be in their foresight. However, Data monitoring is essential to ensure that data is flowing smoothly through the system and that any issues are detected and resolved quickly. Data engineers need to ensure that they  design and implement effective data monitoring solutions that can detect and alert when data quality issues arise. An ideal tool can help them by automatically monitoring data pipelines and alerting the data engineer if there are any issues.

Performance Optimization: Data engineers need to ensure that the data engineering system is performing optimally, especially when dealing with large volumes of data. They have to optimize their data processing, storage, and processes to ensure that the system is efficient and cost-effective. They also need to ensure that the system can handle spikes in data volume and traffic without slowing down or crashing. Automation can help in this area by providing tools that can automatically optimize the performance of data pipelines.

Overall, data engineers need to be skilled in identifying and overcoming challenges in all stages of the data engineering process to build reliable, scalable, and secure data systems. However, they are inundated with daily, mundane tasks and handling this could be difficult. In an ideal world, they need a tool that can automate most of these processes and provide them speed so they can scale their efforts.

How Can Automation Help Data Engineering?

Automation can help data engineers in multiple ways:

  • Data Ingestion and Processing
  • Data Cleaning and Validation
  • ETL and ELT
  • Data Monitoring
  • Performance Optimization

Data Ingestion and Processing: Automation can help data engineers automate these tasks, reducing the time and effort required to ingest and process data. This can be achieved using tools such as Nexla, which allows the creation of complex data pipelines that can automatically ingest data from different sources, such as databases, APIs, files, and streams. Nexla automatically processes and transforms data – eliminating the need for manual data collection and makes the ingestion process faster and more efficient.

Data Cleaning and Validation: Data engineers need to ensure that the data they work with is clean and accurate. Nexla’s automation engine can help them by automatically identifying and fixing data quality issues, such as missing values, inconsistent data types, and data outliers.  It learns your data and intelligently applies smart validations and allows you to mark attributes, e.g. check for values, pattern, type, or any other validation you may need. Nexla can perform data cleaning operations, such as removing duplicates, correcting misspellings, and removing invalid values. This ensures that the data being processed is clean and consistent, reducing the risk of errors and improving decision-making. As a result, Nexla plays a critical part in helping data engineers reduce the time and effort required to clean data.

ETL and ELT: Data engineers can’t be bogged down by making one-off pipelines for different users and need these requests for users to be handled in a self-serve manner. Nexla is one of the few all-in-one tools that provides both ETL and ELT capability in one platform. Through Nexla’s simple to use no-code, low code interface, any user – such as business user, data scientist, data analyst – can do self-service data transformation and validation to create pipelines. They can easily connect to any data sources, using the right authentication, then validate and transform data, and load it to any source. These sources could be databases, data warehouses, applications, or custom applications built by the company.

Data Monitoring and Alerting: Data engineers need to monitor data pipelines to ensure they are running correctly and are producing the expected results. When users create transformations using Nexla, they end up creating data products called Nexsets. Every Nexset  is built with audit trail capability ( auto-versioning) — whether it is a schema change or a modification to a transform function. All user changes and Nexla’s intelligent auto-updates are logged, giving a full audit log view and lineage.

Performance Optimization: Data engineers need to optimize the performance of data pipelines to ensure they run efficiently. Nexla can help data engineers save time and effort, increase productivity, and improve the quality and accuracy of the data they work with. Nexla can process data in parallel, allowing multiple tasks to be executed simultaneously. Nexla can distribute processing tasks across multiple machines, reducing the load on individual machines and improving overall performance. Finally, compression of data during processing helps reduce the amount of data that needs to be transferred and improves data processing speed.

Conclusion

Data engineering is an intrinsic part of any modern business, helping enterprises make better decisions, provide more comprehensive customer experiences, and help identify new business opportunities. As the core of a data solution, the ability to use data for decision-making or insights relies upon accurate data delivered promptly in a usable format. The rise of data engineering as a role filled this gap, but as the amount of data grows exponentially, so too does the work required of data engineers.

By automating common tasks, data processing becomes faster, more accurate, and allows data engineers to focus their efforts where their expertise and creativity can add value. For example, instead of pipeline creation, data engineers can focus on pipeline engineering.

Data engineering is the future, and as the volume and speed of data increases, the speed of data engineers must grow to match. Automation is the next step towards a unified modern data solution, allowing businesses to keep pace with an ever-accelerating world. Powerful tools like Nexla help data engineers to more effectively process data, decreasing up time-to-value while maintaining governance and compliance.

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