Data engineering has exponentially grown over the years due to huge changes in technological advancement, explosion of data volumes, and real-time insight needs by data companies. Data engineering involves designing, building, and maintaining systems that collect store, and analyze data into structures suitable for building analytics solutions. It has existed as far back as the 90s but as a subset of the emerging data technologies and techniques in the analytics landscape. However, with the rise of Artificial Intelligence and Machine learning, the forerunner industry of data analytics and structuring—data engineering—became an independent concept and role for the data industry. This article explores the evolution of data engineering, the shift in the traditional approaches over the years, and the critical role ELT (Extract, Load, Transform) tools, particularly AirByte, play in the modern data ecosystem.
The Early Days of Data Engineering
The primary focus of data engineering in the early days was on building data warehouses using ETL (Extract, Transform, Load) processes. ETL consists of 3 steps of data processing:
- Extract: Data is gathered and extracted from different sources.
- Transform: The gathered data is processed and transformed into a standardised format. This includes sorting, filtering, aggregating, enrichment, joining, cleaning, validating, and other processes to fit the data to its purpose.
- Load: The transformed data is loaded into a designated data store, usually a warehouse or repository.
Data engineers were responsible for extracting data from several sources, transforming it into the desired format, and loading it into a data warehouse. The transformation work in ETL is done in a dedicated engine. It often involves using staging tables to temporarily store data while it is being processed and then loaded to its destination. The strength of ETL lies in its ability to process data from smaller sources and complex transformation. It separates the concerns of the target system from data transformation. It is an excellent choice where data security is paramount, as it can encrypt sensitive data during its transformation process before it lands in the target location—the warehouse or repository.
ETL tools have made data ingesting to a warehouse for analytics possible. However, due to its drawbacks, it is not ideal for high-volume data operations or long-term data projects. Traditional ETL tools do not store data but move new data from a source to a new location, thereby requiring a central repository, which is paid for. Depending on the size of your data and how much it increases with time, your store budget can rise exponentially, burning a hole in your purse. Also, it can be hard to scale the ETL process as your data grows, making it expensive and time-consuming, especially if you need to update or change your system architecture. Batch processing, one of the data analytics processes, also exists as a challenge of ETL tools. Processing batch data with ETL tools causes time lags between data extraction and availability. As a result, you won’t have access to real-time data insights, preventing you from making timely business decisions. Another challenge of ETL tools is their complexity and learning curve. Although there are low-code tools, low-code developers and non-technical users cannot easily start a project as they require a knowledge of programming, data engineering, and ongoing maintenance.
The Shift to Modern Data Engineering
Data volume growth reduced the feasibility of traditional ETL approaches due to scalability issues. The advent of big data technologies, such as Apache Hadoop and Apache Spark, allowed data engineers to process massive datasets in a distributed manner. This led to adjusting the ETL data processing approach from a one-by-one method to parallel processing. For instance, while data extraction is ongoing, the transformation process could be performed on already extracted data, and processed data are loaded, all running concurrently. ETL parallel processing significantly improved the efficiency of handling large datasets, notwithstanding, it was not enough. More businesses began to demand real-time insights, leading to the adoption of stream processing technologies like Apache Kafka and Apache Flink. This shift required data engineers to build more flexible and responsive data pipelines and storage systems. One of these storage systems is the cloud.
The adoption of the cloud revolutionized data engineering by offering scalable storage and computing power, which relieved the load of traditional data pipelines. Cloud platforms like AWS, Google Cloud, and Azure introduced managed services that reduced the burden on data teams, enabling them to focus more on data transformation and analysis. As cloud computing developed and data warehouse technologies became more complex, the data processing ecosystem gave rise to the ELT (Extract, Load, Transform) paradigm.
The Emergence of ELT Tools
The shift towards ELT architecture led to specialized tools and platforms to streamline data integration. Companies like Fivetran, Stitch, and Airbyte became leading suppliers of ELT solutions, offering prebuilt-performed connectors and automated data pipelines. These newer tools have simplified connecting data sources to cloud data warehouses, making the ELT more possible for organizations of all sizes.
Compared to ETL, ELT only reverses the latter two steps: data from several sources is loaded directly into a data warehouse or lake before being transformed. Within the ELT pipeline, transformations are executed inside the target data store using its native processing capabilities without depending on a dedicated transformation engine. ELT offers more solutions than ETL in terms of flexibility, scalability, efficiency, reduced latency, and storage processing.
Using ELT tools, data can be loaded in its raw form and transformed as needed, making it easier to adapt to changing business requirements. It gives businesses an edge when making decisions as they don’t have to worry about transformation logic until later. Leveraging the cloud for transformation, ELT tools are faster and more scalable. ELT takes advantage of the cloud data warehouse's key features such as Massive Parallel Processing (MPP), elastic computing, separation of storage and computing, and built-in optimization, pools them together, and coordinates them easily to handle complex transformations at scale. By deferring transformation, ELT can reduce the time it takes to make data available for analysis, making it efficient and effective for fast decision-making. Also, ELT is better suited for handling any data type—unstructured, semi-structured, and structured, allowing you to focus on more important details during transformation rather than data structure. In addition, it is cost-effective, tapping the cloud pay-as-you-go model, has a faster initial data loading and availability, and offers a simplified architecture by removing the need for intermediate transformation layers.
The ELT paradigm has been particularly valuable in organizations with large volumes and diversified data types, where rapid access to raw data is necessary for different analytics purposes. As cloud data warehouses evolve, ELT’s contributions towards modern data architecture have become central to effective data management strategy.
AirByte: A Key Player in ELT
AirByte is an open-source ELT tool that focuses on making data integration easy and accessible. Its community-driven approach and the importance of open-source contributions have been pivotal to its growth, enabling the rapid development of new features and connectors. It supports numerous connectors, allowing data engineers to move data from various sources to destinations quickly. AirByte’s open-source nature enables users to create custom connectors, making it highly adaptable to unique business needs.
AirByte stands out due to its flexibility and community-driven development. It also offers scalability, customizability, and ease of use.
- Scalability: Leveraging its cloud infrastructures to handle large data volumes, AirByte ensures smooth scalability as the business grows.
- Customizability: Users can customize existing connectors or build and contribute their own, making AirByte suitable for integrating less common data sources. You can also choose AirByte’s cloud for your organization, an external cloud storage facility, or even a self-hosted cloud option.
- Ease of Use: AirByte's user-friendly interface and wide array of pre-built connectors simplify data integration for engineers and non-technical users.
Unlike traditional proprietary tools, Airbyte is open-source, enabling organizations to inspect, modify, and contribute to its codebase for optimal transparency and community-based development. Airbyte's power emanates from having a rich library with over 300 pre-built connectors, ranging from common databases and cloud services to specialized business applications. It considerably reduces engineering effort in setting up data pipelines since it handles all complexities from the extraction of data up to loading. Airbyte is designed to support real-time synchronization, incremental updates, and automatic schema detection, making it adaptive to changing data structures.
The Modern Data Stack
The modern data stack represents a paradigm shift in rethinking data architecture: modularity, scalability, and tool specialization. This has changed the game for organizations, enabling them to rethink how they handle their data pipeline needs by moving away from monolithic solutions to a flexible and maintainable architecture.
Separation of Concerns
The evolution of data engineering does not end at the switch from ETL to ELT tools. Rather, it has led to the modern data stack, where extraction, loading, and transformation are separated into distinct layers. Airbyte, as an ELT tool, takes care of the general movement of data, enabling other tools like dbt to focus on the necessary transformation step to get the data analytics-ready. This separation means that organizations can:
- Choose best-in-class tools for each function
- Scale different components independently
- Maintain and update components without affecting others
- Reduce complexity by using specialized tooling
- Allow various teams to work side-by-side on different aspects of the pipeline
Data Lakes and Warehouses
Data Lake and Data Warehouse integration allows organizations to store their raw and transformed data within a common architecture, thus allowing Data Science and Business Intelligence use cases. Airbyte plays a key role in populating these data lakes and warehouses with raw data from several sources. This hybrid approach offers:
- Flexibility in terms of structured and unstructured data storage
- Low-cost storage of history data
- Support for various analytical workloads
- Better Data Discovery and Governance
- Ability to serve multiple personas of users: data scientists, analysts, business users
Orchestration and Governance
Modern data stacks require a complex orchestration to manage data flow from one component to another. These coordinations or orchestrations are performed with tools like Apache Airflow or Dagster by:
- Pipeline scheduling and dependencies
- Error handling and monitoring
- Data quality checks
- Resource allocation
- Workflow management
Data Quality and Testing
For modern stack, the emphasis is on data quality in the following ways:
- Automated Testing of Transformations
- Data validation at multiple stages
- Data freshness and completeness monitoring
- Documentation of data lineage
- Implementation of data quality frameworks
Analytics and Business Intelligence
The last layer of the modern data stack focuses on making data accessible to end-users through self-service analytics platforms, interactive dashboards, machine learning models, ad-hoc query capabilities, and automatic reporting systems. This modular approach to data infrastructure has democratized access to sophisticated data processing, enabling even the smallest organizations to build robust and scalable data platforms that grow with their needs.
The Future of Data Engineering
In the times of ETL, low-code, no-code, and non-technical developers due to a certain level of required programming knowledge to kickstart a project. This changed with ELT tools, especially AirByte which provides an easy-to-use interface that allows non-technical users to build or customize a data pipeline. The future of data engineering promises low-code and no-code developers pre-built templates for common data workflows, automated code generation for custom requirements, simplified debugging and monitoring tools, visual pipeline builders replacing complex coding requirements, and more.
Data quality and governance are gaining more attention as data becomes increasingly critical to businesses’ decision-making and AI/ML. Integrating AI/ML into data engineering transforms how we handle, process, and maintain data pipelines. AI and ML are transforming data engineering into predictable self-healing pipelines, leveraging predictive maintenance to detect or foresee failures before they occur. On the subject of data quality, this includes AI-driven anomaly detection to ensure data reliability, and intelligent metadata management to enable automation of cataloguing and lineage tracking. For businesses, Airbyte's roadmap includes features to ensure data accuracy, compliance, and lineage for operating in regulated industries.
Conclusion
Data engineering has incrementally moved from rigid, batch-oriented ETL to flexible, cloud-based ELT pipelines. AirByte positions itself at this forefront of change by providing the means of building scalable, elastic, real-time data pipes with much ease. As data engineering evolves continuously, it will increasingly focus on automation, quality, and making data engineering accessible to a broader audience.
See the documentation to learn more about setting up data integrations using AirByte. You can also join the community to stay updated on the latest data engineering changes.
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