ETL Tools—Key Features to Consider in The Post-AI Era

Data no longer exists only in traditional databases but throughout cloud services, APIs, and streaming platforms, among others. Conventional ETL processes, with manual coding and strict workflows, cannot keep up with the dynamic nature of modern data environments.

This article explores the fundamental principles and logical architectures to consider when choosing an ETL tool. 

Summary of features in ETL tools

Key features to look for in ETL tools

The list below will help you evaluate the different tools in the market today.

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Data requirements

Business requirements regarding data processing are a key factor when choosing ETL tools. Organizations should consider the volume of data that needs to be processed, the speed at which it needs to be done, and the variety in its structure while deciding on an ETL tool. 

Small datasets require less complex tools and can be managed with a simpler solution. However, handling terabytes or petabytes of data requires robust, scalable solutions that can adapt to volume and velocity. The data structure also determines the logic behind chaining the jobs together for optimal configuration. An ETL tool with comprehensive orchestration facilities is key to defining job dependency configurations. 

For certain time-critical use cases, applications rely on up-to-the-minute data, and your ETL tool should support real-time data processing. While batch processing requires scaling resources up and down on schedule, real-time processing requires scaling resources according to load patterns. 

Integration and compatibility

The ETL tool should seamlessly integrate with your existing systems and third-party tools. Supporting a wide range of source and target technologies gives flexibility to manage and utilize data.⁤ For example, 

• Databases: Support for SQL databases (e.g., MySQL, PostgreSQL), NoSQL databases (e.g., MongoDB, Cassandra), and NewSQL databases (e.g., Google Spanner).
• APIs: Connectivity to RESTful services and SOAP endpoints.
• Flat Files: Handling of CSV, JSON, and XML files.
• Streams: Real-time data feeds and IoT device outputs.

Vector database support has become essential for applications involving LLMs and complex data types like embeddings. For example, a recommendation engine will use a vector database to implement product suggestions based on the user behaviors in the system. Ideally, you want a tool to move data from any database to the vector database and keep it updated periodically or in real time.

Transformation capabilities

Advanced transformation capabilities are essential for the effective functioning of the ETL processes. Look for tools that offer data cleansing, enrichment, and complex computations. Adopt Data as a Product approach, which treats every dataset as a consumable product with its lifecycle for collaboration and efficiency.

Transformation functionalities and error-handling mechanisms ensure data accuracy and reliability.

Scalability

As your data grows, ETL tools must support growth without impacting performance as much as possible. Supporting this kind of growth demands two forms: horizontal scaling, which adds more machines, and vertical scaling, which increases the capacity of existing machines.

Ease of use

An easy-to-use interface reduces the learning curve and accelerates adoption across the organization. Tools that support no-code/low-code options enable business users to develop and manage data workflows without requiring deep programming knowledge, thereby increasing accessibility.

Collaborative workflows with version control and shared workspaces are conducive to productivity. For instance, business analysts can design and modify the ETL pipelines to produce reports without waiting for an IT team’s support; this is quite efficient and responsive to business needs.

Performance and efficiency

Processing speed is critical to ensure data processing tasks are completed within acceptable time windows in batch and real-time modes. Tools need to be strongly focused on resource optimization to minimize operational costs.

Automated monitoring should accompany continuous performance tracking to identify bottlenecks in advance. For example, a shipping company may require timely data processing to find the optimal route to get the shortest delivery time in real time while minimizing fuel consumption.

Data quality

The ETL tools must include automated error correction mechanisms to make data validation efficient. Data enrichment includes additional information to make data more useful. For example, enriching a customer database with geolocation could enable a retail chain to formulate schemes for promotion based on regional trends. Make sure that the ETL tool supports this.

Data governance and monitoring

Robust monitoring capabilities would ensure that data pipeline health is always maintained. ETL tools must provide real-time alerts and dashboards to maintain continuous monitoring. Error detection and logging are required to support troubleshooting and auditing for compliance.

Your ETL tool should also support metadata and data lineage tracking for auditing purposes. The tool lets you associate data about data (who created it, when, how it was transformed, etc) with any given dataset. It should also let you track transformations over a given period for root cause analysis and auditing over a given period.

Security

Security is an important selection criterion in ETL tools. Ensure the tool supports data encryption with high encryption standards. Also, ensure that the tool can support the implementation of compliance policies. Adherence to industry-specific regulations and best security practices, such as SOC 2 for data protection, HIPAA for healthcare privacy, and GDPR for data privacy in the EU, is crucial to maintain the integrity and confidentiality of sensitive data.

For example, implementing role-based access controls (RBAC) means that roles restrict tool access. This feature is a must to meet HIPAA (health care) regulations.

Cost considerations

Understanding the cost structure of ETL tools is essential. There are subscription-based, usage-based, or a combination of pricing models. Factor in the Total cost of ownership (TCO), which cuts across licensing, implementation, training, maintenance, and scaling. A startup, for instance, will most likely prefer a cloud-based ETL tool with a pay-as-you-go model that relates cost to growth.

Support

Consider the support level the tool vendor provides. An active user community and resources like documentation tutorials, webinars, ebooks, and guides can help your team resolve issues faster and learn best practices.

Architectural patterns in ETL

Understanding data integration architectural patterns is important in designing efficient and scalable data pipelines. Some patterns in ETL pipeline implementation include:

Lambda architecture

The lambda architecture combines batch and real-time processing, giving a holistic data-processing solution. It contains three primary components.

1. The batch layer works with big volumes of data to draw historical analysis or long-run trends and patterns. 
2. The speed layer handles real-time data.
3. The serving Layer combines the outputs from the batch and speed Layers to offer a unified data view.

For example, a social media platform uses lambda architecture to determine user engagement trends over time and parallel servicing of real-time content recommendations. This would mean the platform supports historical insights and up-to-the-minute recommendations for a better user experience.

While implementing Lambda Architecture, ensure that the batch and speed layers coordinate so as not to lose data consistency. You can also leverage distributed computing frameworks (Apache Hadoop, Apache Storm, etc) for efficient processing.

It’s also important to be aware of edge cases, such as handling late-arriving data in the speed layer, which requires additional mechanisms for data accuracy and completeness.

Kappa architecture

Kappa architecture treats all data sources as streams and thus provides a more streamlined pipeline as compared to Lambda architecture. This pattern helps in making recent data available as soon as possible for user queries. The streaming data is archived to facilitate historical queries. Implementing this requires a stream processing framework like Apache Kafka, Flink, or AWS Kinesis. 

The advantage of Kappa architecture is that it uses a single technology stack for real-time and historical queries. If a recomputation over historical data is required, the entire stream is replayed, and data is fed through the original path. It does away with the separate serving layer for real-time and batch processing and provides a single real-time view. 

Kappa architecture high-level view

Microservice architecture

Microservice architecture breaks down ETL processes into small, independent services that communicate through APIs. This structure enhances scalability, flexibility, and resilience, as each service can operate, scale, or be updated independently without impacting others. It also helps isolate faults, meaning issues in one service don’t disrupt the entire ETL pipeline.

For instance, an online retailer might use microservices to handle independent ETL tasks for the inventory, the sales data, and the customer data. With such separation, the retailer can scale the inventory service during peak periods without affecting the sales or customer data services. 

Emerging trends

Data as a product

Advanced ETL tools allow you to represent data as virtual data products that authorized users can access, maintain, and work with. These products are not data copies but a way of organizing data by schema. The tool automatically updates the schema and versions it for currency and relevance. You can find data, apply validations, and automate error management for each product.

Treating data as a product increases collaboration among departments. It breaks down the silos and enhances the coherent data culture of organizations. It also promotes data lineage, tracking the origin and transformations of data to maintain integrity and data compliance.

No-code/low-code

No-code/low-code is changing the way organizations think about ETL. It no longer requires a developer to create or modify the ETL pipeline. Business users who are closer to data can compose complex data flows without extensive programming knowledge. For example, a business analyst could change data workflows according to marketing campaigns. You get more agile responses to changing business requirements.

Support for vector databases

Vector databases store and handle high-dimensional vectors required for LLM training and RAG workflows. Next-generation ETL tools allow you to move data to the vector database directly from the source. They allow you to process and perform many operations within high-dimensional data, including similarity searches and transformations on vectors. They increase the speed of AI adoption within your organization.

Exploring ETL tools

Apache Airflow

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. Launched in October 2014 by Airbnb, it later became an Apache Software Foundation project. Airflow allows users to define workflows as directed acyclic graphs (DAGs) of tasks. DAG gives flexibility and control over workflow execution, making it usable for simple ETL tasks and complex data pipelines.

A DAG represents all the tasks you want to execute in the structure of their relationships and dependencies. You can run tasks in a way that maximizes parallel execution. 

Airflow’s scheduling mechanism specifies the execution frequency of workflows in terms of cron-like expressions. There are many built-in operators for databases, cloud services, and APIs. Bi-directional flows are supported, and custom data sources can be integrated.

The platform is also effective for dealing with complex transformations. You can write custom Python code and external scripts and integrate Airflow with frameworks such as Apache Spark and Hadoop. 

However, this flexibility and scale come at the cost of complexity. Teams without dedicated DevOps resources can struggle to learn and manage the tool. Resource requirements to deploy and maintain are quite substantial. Users have to manually manage schema modifications and data enrichment.

Apache Beam

Apache Beam is an open-source project that provides a unified programming model for designing and executing batch and streaming data-parallel processing pipelines. Its strength lies in its ability to run pipelines on several execution engines, including Apache Flink, Apache Spark, and Google Cloud Dataflow. Developers can author their data processing logic once and then drive it into the execution environment that best fits their needs and constraints.

One of the core concepts behind Apache Beam is using one programming model to handle both batched and streaming data: this reduces the development complexity and the learning curve for developers. The model is very expressive, and you can run complex data transformation, windowing, and stateful processing. 

The downside is that it is a code-based tool requiring developers and DevOps expertise to implement.

Prefect

Prefect is a relatively new ETL tool that streamlines the effort in developing, executing, and monitoring data pipelines. It offers hybrid model implementation to execute workflows in the cloud and on-premises. It also has a robust scheduler, making scheduling complex workflows easy with the same SDK. The tool is user-friendly while remaining code-centric, as the developers can utilize their coding skills to create customized workflows. 

Dagster

Dagster is an open-source orchestration platform for machine learning, analytics, and ETL workloads. It emphasizes data assets and their lineage. Users can trace the flow and transformation of data throughout data pipelines.

One of the major concepts in Dagster is software-defined assets. An asset is defined as anything that a pipeline run can produce. This means that users can take responsibility for defining and managing their data assets explicitly for more control and visibility over the full lifecycle. Dagster’s system validates every data type at every stage of the pipeline, minimizing the risk of error and enhancing data integrity. 

However, Dagster is designed for Python developers and may not fit all teams well.

Nexla

ETL is just one aspect of Nexla, an enterprise-grade all-in-one data integration platform. It comes with comprehensive connector support for many sources and destinations. You can use it to unify disparate data sources, including on-premise, hybrid cloud, edge computing sources, and IoT. Nexla enables easy access to data from disparate systems while embedding governance and security measures.

A unique feature of Nexla is Nexsets—its way of managing and abstracting complex data sources. Nexsets make it easier for users to work with diverse data types without needing to handle the details, powered by Nexla’s metadata intelligence layer. Nexsets include a comprehensive toolkit for transformations, validations, filtering, and documentation, giving users a consistent, easy-to-use interface regardless of data schema, format, or speed.

Nexla supports both batch processing and event stream processing. It also comes with job scheduling support and enables defining branched job flows through acyclic tree structures called Nexla data flows. Nexla data flows support auto-scaling. 

As a platform, Nexla can be used as a completely managed service that is deployed in customers’ cloud or even on-premise. Nexla comes with a large list of pre-built transformations, including AI vector embedding generation.

Nexla’s no-code development features enable analysts and other business users to use its comprehensive connector support and prebuilt transformers. The platform facilitates the quick development of AI RAG workflows through its low code interface. And in case you need more flexibility, Nexla provides a range of APIs for custom development. 

Nexla Orchestrated Versatile Agent, or NOVA for short,  can help developers implement transformations through natural language prompts. It can generate Python or SQL transformation scripts based on prompts, take feedback from developers to improve, and then deploy them. NOVA can also suggest transformation logic based on the context while developing data flows. For example, while working with an access-controlled data set, NOVA can prompt the developer to add a PII data masking step and generate the script automatically upon approval.

Nexla has a full suite of monitoring and notification features for mission-critical jobs. Its automatic error reporting and flexible management options help debug dataflows quickly.  

Nexla’s automatic lineage tracking, audit logs, PII data masking, and granular access control configurations enable easy governance and security policy enforcement. 

Summary of tool features

We provide a high-level comparison below.

Conclusion

Choosing the right ETL tool is more than just a tactical decision – it’s a strategic investment that will shape your organization’s data capabilities. The best tool is the one that not only meets your current needs but can also scale and adapt as your business grows. By selecting a solution built for flexibility and long-term value, you position your organization to harness data effectively, drive innovation, and achieve lasting success.