ETL Tools for Fast and Reliable Data Management

Attributes of ETL tools

Depending on the type of data in your organization, you can employ different kinds of ETL tools to suit your business objectives. In fact, the top ETL tools now use modern technologies such as AI and machine learning to automate different parts of ETL. This reduces manual tasks and the risk of human error. 

A few of the main attributes to look for include:

Batch processing vs. streaming

Batch processing refers to the process of extracting, transforming and loading data in chunks and at intervals. This is an excellent tool for processing large amounts of data efficiently and ideal for when data does not need to be processed in real time. 

Streaming means that data is processed as it is received. This continuous data extraction is best for organizations that need up-to-date information, such as in a live dashboard or real-time monitoring and alerting.

Some ETL tools can handle both batch and streaming processing, while others are more tailored for one or the other.

On-premises vs. cloud

On-premises ETL runs within an organization’s own infrastructure. This model gives you full control over data security, storage and processings, but it also requires significant hardware and maintenance costs.

ETL tools in the cloud offer businesses more flexibility and cost efficiency, and they integrate seamlessly with cloud storage solutions and cloud data warehouses. These tools often come with built-in automation features to make data processing more efficient.

Open source vs. proprietary

Open source tools are ideal for businesses that want flexibility and customization. Engineers can study the source code to determine the precise build of the infrastructure and modify its capabilities to best suit an organization’s data needs. Keep in mind that the documentation and functionality may vary in these tools, since they are often created by a decentralized group of developers.

There are numerous proprietary ETL systems. Since these tools are backed by major organizations — and not a diffuse group of engineers — customers usually benefit from smoother implementation and maintenance, as well as customer support and frequent updates. 

No-code/low-code vs. codable

No-code tools are great for less-technical users or those without data engineering experience. Instead of code, these use graphical user interfaces (GUIs) where users can drag and drop components to design ETL workflows. While they aren’t as customizable as low-code tools, they do offer simple solutions for organizations that need quick and simple data integration tools.

Fully codable ETL tools allow users to take full control over their ETL pipelines and write custom code to define precise instructions for their data. These tools are designed for more technical users like data engineers or developers.

In the middle lie low-code ETL tools, geared toward semitechnical users who may have some coding experience but who want to avoid extensive manual coding. Low-code ETL tools provide a mix of visual interfaces and coding options. Users can still build workflows with drag-and-drop elements but also have the option to customize certain steps through scripting or pre-built code snippets.

Capabilities to consider when comparing ETL tools

ETL platforms come with many useful features, and it’s important to find one that fits an organization’s goals. Let’s look at some of the main capabilities to consider when comparing ETL tools.

Ingestion

Ingestion is the process of importing and cleansing data before storage. Effective ingestion is essential for receiving quality data. An effective ETL tool should be capable of identifying inaccurate, duplicated or irrelevant data, and take specific steps to resolve it. This ensures that an organization receives the most accurate insights in their reports.

To ensure effective data ingestion, look for the following features:

• Change data capture: Refreshes tables to reflect any changes to source data
• Streaming ingestion: Gradually populates data into the appropriate layer for pipelines or SQL queries
• Connectivity to multiple sources: Including cloud storage, external systems and message buses 

Any ETL tool must also effectively replicate and synchronize data from the original source to the target source. This can occur in real time or in periodic updates, and it can copy the entire dataset or simply the changes or updates to existing data.

Extraction

While ingestion imports a dataset, extraction retrieves it. The two may overlap, as you may be ingesting data as you extract it. For example, you might extract behavioral or product data from a website before it is ingested and sent through the pipeline.

As with data ingestion, extraction should involve standardizing data and removing errors. It should also ensure that data is collected in a way that is compliant with data privacy legislation.

It’s important to choose scalable types of ETL tools. You may need to ramp up your extraction activities if you begin collecting data on a wider scale. The speed and effectiveness of ETL shouldn’t be impacted when extracting larger datasets.

Transformation

During the data transformation stage, ingested data is made compatible with various applications. Organizations will outline a specific schema for the transformation process to follow. When complete, transformed data should meet these specifications.

The transformation process can be complex. Make sure you’re equipped with the following features to get the results you need:

• Data quality checks. These guarantee that data input into tables meets quality standards. For instance, Databricks Delta Live Tables has a feature called Expectations, which ensures data quality checks are applied to every record passing through a query. 
• Easy-to-understand architecture. This ensures that data is stored in a logical and accessible manner. For instance, with the medallion architecture, data quality is incrementally improved as it passes through each layer.  
• Different update modes. Options such as triggered mode (where processing is halted after refreshing tables, ensuring updates are in line with available data at the start of the update) and continuous mode (where new data is processed as soon as it arrives) allow for better control over your data.

Load

The final step in the process, the load function, writes converted data from a staging area to a target database. These destination points are called data sinks, and they provide a central location for you to store and process your data. Depending upon a user’s needs, data sinks can load to different destinations such as databases, data warehouses, data lakes and even cloud services.

It is important to look for the following in any data sink:

Scalability: As organizations accumulate increasing amounts of data, the data sink must scale to accommodate larger datasets. Scalability makes sure that the data sink can handle a growing number of operations without slowing down.

Reliability: Trust and reliability are essential in a data sink. This ensures that it can store and retrieve data without loss or corruption and can quickly recover from any potential failures.

Security: Data security ensures that sensitive data is protected from unauthorized access, breaches or leaks during the sink process.

Interoperability: It’s also important that data sinks are prepared to work with various data sources and destinations. Any data sink should be compatible with other ETL tools, reporting tools and other software.

Orchestration and automation

Pipeline orchestration is the process of transferring and managing data throughout the ETL process. The construction of effective pipelines helps to ensure that data can be transferred smoothly and efficiently. This provides easy access to key information and elevates a business’s ability to make on-the-spot, data-driven decisions.

You can measure a solution’s orchestration capabilities by its ease of use. Look for a fully managed orchestration tool, which should eliminate manual maintenance, updates and troubleshooting.

Many data professionals prefer an ETL tool that can:

• Provide native workflow authoring within a workspace
• Offer automated data lineage for each workflow
• Easily define workflows with a few clicks
• Offer enhanced control flow capabilities such as conditional execution, looping, etc.

Managing and transforming data is highly time-consuming, which is why data automation is so useful. With the right solution, a user simply has to define the transformations they’d like to perform. The ETL tool will do the rest of the work including automatically handling tasks such as cluster management, task orchestration and error handling.

The best ETL tools automate all aspects of operational complexity. For instance, Databricks’ Delta Live Tables has the following features:

• Failure handling and error recovery 
• Task orchestration
• CI/CD and version control 
• Autoscaling compute infrastructure

Observability and troubleshooting

As ETL tools usually run in batches, some processes will likely run less effectively than others. That’s why it’s essential to have full observability over ETL tools, enabling you to spot errors and inefficiencies quickly.

The best ETL tools provide the option of setting notifications to alert teams about errors. Your solution should also provide access to performance metrics and visualizations.

Using an ETL tool, you should be able to access the following:

• Insights into data freshness 
• Identification of anomalies within the data schema  
• Understanding of how data is allocated throughout different ETL processes and tools

Selecting a proven ETL supplier will minimize system problems. There may, however, be rare occasions when your ETL system doesn’t function correctly. When this occurs, it’s important to solve any issues as quickly as possible to prevent data errors. That’s why an ETL tool should come with comprehensive troubleshooting capabilities. Alongside this, a provider should be accessible if you need a guiding hand.

It’s always best to choose a tried and tested solution with an active community. Reliable ETL providers maintain community forums. These enable users to post problems and receive advice from a mixture of brand representatives and community members.

Streaming

Data streaming is the continuous flow of data from many sources into your system. This approach has its advantages. Since there is a constant stream of data, you always have access to the most up-to-date information, allowing you to make quick decisions based on current trends.

ETL systems that deploy data streaming can provide real-time analytics. For example, with Databricks Workflows you gain access to massive pools of data in motion. By analyzing this data, you can spot new opportunities as well as identify potential risks.

Streaming real-time data is also a useful way of training your machine learning models. With a constant stream of quality data, you can build production-quality ML applications.

Future-proofing

When choosing ETL tools, it’s important to plan ahead. The data needs of your business today likely won’t be the same as its needs tomorrow. Aim for a solution that meets your long-term needs. An ETL system should support some of the following future-proofing features:

• Autoscaling: The option to adjust the size of clusters based on changing data volumes. This process should optimize cluster utilization while reducing end-to-end latency.  
• Flexibility: Look for solutions that have flexible integration capabilities. This will ensure continued effectiveness as new products and technologies become available. The ability to integrate with open source technologies is particularly useful. 
• Scalability: As your business grows, your systems and tools need to grow in tandem. Make sure that solutions are easily scalable, without the need for a huge overhaul. 
• Configurability: An easily configurable tool will simplify the handling of requirement changes. These could come from internal strategy or external regulations.

TCO and ROI

ETL tools can vary in price, so it’s crucial to examine both the total cost of ownership and the overall benefits to your organization and its bottom line. The purchase of an ETL tool is only the beginning of the process. Some additional cost factors to consider include:

Software/subscription costs: Whether a business chooses open source or proprietary tools, prepare to make an initial investment in the software for ETL tools.

Infrastructure costs: This includes the cost to establish the hardware, cloud services and/or data storage infrastructure needed to run an ETL tool.

Implementation and training: Staff will need to spend significant time configuring, integrating and tailoring the ETL system to an organization’s specific needs. Additional staff will also need to be trained to manage and maintain the ETL tools, which can lead to lost productivity in other areas.

Maintenance and support: Given the accelerating pace of data collection, ETL tools need ongoing maintenance and support to ensure data is optimized and useful.

By making an up-front investment in ETL tools, an organization is committing to improved data integration and analytics capabilities. Some of the ways you can expect your organization to see a return on investment from ETL include:

Improved data quality and accuracy: Working with a trusted dataset allows you to make sound business decisions faster and with more confidence.

Faster time to insight: ETL tools can speed data processing and give key decision-makers access to data more quickly. In a competitive business environment, making decisions based on reliable, high-quality data can give your organization an edge.

Scalability: As your business scales ETL tools can also grow, handling increased volumes of data and improved performance.

Databricks ETL capabilities

ETL tools are critical for any business that handles medium to large-scale data collection. To get the most out of your data, it is crucial to prioritize a modern, forward-thinking approach that embraces machine learning and the power of data intelligence.

Databricks Delta Live Tables simplifies ETL development by codifying best practices out of the box and automating away the inherent operational complexity. With DLT pipelines, engineers can focus on delivering high-quality data rather than operating and maintaining pipeline infrastructure.

For orchestration of ETL pipelines and beyond, Databricks Workflows provides you with a single solution to orchestrate tasks in any scenario on the Data Intelligence Platform. Workflows lets you easily define, manage and monitor multitask workflows for ETL, analytics and machine learning pipelines. With a wide range of supported task types, deep observability capabilities and high reliability, your data teams are empowered to better automate and orchestrate any pipeline and become more productive.

As businesses work to remain competitive, it is important that decisions are based on up-to-date, quality data and insights. Leveraging quality ETL tools can lead to significant cost savings and data-informed decisions that drive revenue.

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