What are modern ETL tools?

ETL is now a seasoned methodology, having emerged in the 1970s when organizations began to join up different databases. In the following decades, ETL – and the tools used to execute it – have inevitably evolved. Today, we take a fresh look at ETL software, as well as the benefits of some of the modern tools available.

What is the latest ETL tool?

There are actually many different tools using the latest data technologies. To compare ETL tools directly, you need to make sure they have particular features. The overarching characteristics of a modern ETL tool are the ability to easily scale, and compatibility with as many standards and formats as possible.

Modern ETL tools can import and export both structured and unstructured data. They can extract data from almost any source, such as smart home devices or physical checkout systems. They also support both on-premises and cloud-based data warehouses, such as Google BigQuery or Amazon Redshift.

ETL software must support real-time streaming data pipelines and real-time schema changes to minimize access downtime for your data analysts. The tools should also allow flexibility. For example, enabling you to choose between ETL and ELT (where the data is loaded into the data warehouse before it is transformed).

While you may think you need proprietary software to take advantage of all the features of ETL, there are many good open-source ETL tools to consider including in your data pipeline, such as Airbyte, Apache Camel, and Apache Kafka.

Which ETL tool is used most?

ETL pipelines usually consist of a number of different modules connected together. However, the most popular tools are probably the fundamental programming languages used to build and connect these modules: SQL and Python.

SQL is a query language used to search and update data warehouses. However, SQL doesn’t have the facility to access datasets from disparate systems, so you need to transfer these into a warehouse first. SQL is a language often used as part of an ETL pipeline, but does not constitute the whole pipeline.

Python is a versatile language that can be used for many different applications including building ETL pipelines. But, as is the case with SQL, it can’t be used alone. Building an entire ETL pipeline in Python requires many different resources, such as data engineers, deployment, version control, and security patches. To ensure the pipeline continues to work, you also need to maintain these components effectively while ensuring that they work together.

How do you code ETL in Python?

Of the available ETL tools, Python is probably the most widespread. It’s often used to connect other pieces of software or even build open-source tools, so it makes sense to learn it first. Many of the best Python ETL tools use an open-source module called ‘Pandas’, which adds support for multi-dimensional arrays, data analysis, and ML.

Here’s the outline of how to build an ETL pipeline using Python:

• Hire engineers – The most important step! Depending on the volume of data, this may be a single engineer, or a whole team.
• Install the required modules – In addition to Pandas, common packages that are useful for ETL include MySQL Connector and Firebird.
• Procure the credentials – These are needed to access the data sources, and must be available within your Python environment.
• Write the extraction code – This includes individual functions for extracting data from each format, such as XML, JSON, and CSV.
• Write the cleaning code – ‘Data cleaning’ refers to the process of fixing or removing records which are invalid due to corruption, incorrect entry, incorrect formatting, duplication, or incompleteness.
• Write the transformation code – This applies the required transformations to the data.
• Write the loading code – Load data into the target destination, which is usually a data warehouse.
• Perform ETL testing – Test the data pipeline’s output and performance, then analyze the results.
• Set up monitoring and alerting – This will inform you of any failures, and enable you to manage server resources and scaling.