Data Pipeline Automation

Introduction

Data is the lifeblood of any business. It powers the decision-making, sets strategies, and helps understand customers better. However, managing data has become a challenge for most companies due to the sheer volume of data or the poor quality of the data.

But with the right data pipeline automation system in place, you will be able to clean and prepare the data to better their product and service, allowing your company to grow much faster.

This article will first define what a data workflow is, why we should automate them, and what type of tools are currently out there. Then, I will discuss criteria to help pick a data pipeline automation software and cover the most popular data workflow systems.

What is a data pipeline?

Data pipeline automation converts data from various sources (e.g., push mechanisms, API calls, replication mechanisms that periodically retrieve data, or webhooks) into a specific format to be stored or for other analysis tools. Each pipeline usually involves one of three goals.

1. Cleaning

Data cleansing removes all unwanted characters, e.g., trimming out excess spaces, separating records with semi-colons, and removing newline characters.

2. Replication

We can store data in different files, databases, and clouds with replication. This allows us to give data at a consistent and scheduled time, renaming columns in transition, changing data types if needed, or just transferring the data to the next phase of the data pipeline.

3. Understanding of data

Another critical aspect is to get a clearer understanding of the data, meaning you are removing empty values or binning a bunch of salaries to get a better picture of how many people are in a certain salary bracket.

Sometimes you talk to the data scientists or analysts about how they want to see the data to use it in their analysis and algorithms.

Why should we automate these data processes?

The primary reason to automate data encapsulating is to save businesses time and money. Rather than manually performing data entry tasks, an automation system can help ensure that business databases are always up-to-date. This ensures that businesses will have access to accurate and timely information, which will allow them to make better decisions. Imagine a company that needs to process 1 million data points to generate a report by the end of the week. The company would have spent all of its money, time, and resources on people who need to analyze this data. Automating this data pipeline would free up manpower to perform other necessary tasks and save money on hiring more people or outsourcing work.

A secondary but essential reason is that automating data pipelines provides insight into the data. Specific analytics tools offer data profiling, which lets you see the percentage of empty cells in columns and rows, the distribution of values in columns, and the uniqueness and relevance of values for given columns. This helps you identify any rows or columns with missing or erroneous data points which might have been missed during extraction or provide initial insight as part of the data analysis.

What are the Types of Data Workflow Automation Tools that exist in the market?

The two types of data workflow automation tools are low-code or no-code and code-based. Let’s look at each of them independently.

Low-code or no-code solutions

These solutions let you automate your data workflows by dragging and dropping the desired components from a list of available options onto a graphical representation. Then, you can configure connections between the pieces to automate your data workflows.

Code-free data pipeline automation tools are easy to use and enable non-technical teams to create pipelines and data flows that capture valuable insights from multiple business operations and customer interactions. The learning curve is usually low too, so you can get started quickly.

Although many of these code-free tools are easy to use, they have disadvantages. Most of them lack customization options, so you are stuck to the tool’s limitations i.e. you have to structure your data to work and be limited to whatever they offer regarding data cleaning.

The lack of a source or version control system is often a problem. Without a source or version control system, it can be hard to be transparent among team members. And without such protections, mistakes can take a long time to find and fix and waste more time than necessary for programmers.

These code-free tools have limited integrations with sources and targets, e.g. Fivetran is missing the Salesforce Commerce Cloud integration.

This might not necessarily be a disadvantage to most companies. However, if you are trying to handle streaming data, most of these tools cannot handle real-time data; they can make it difficult to promptly provide the necessary information when needed.

Examples: Fivetran, Alteryx, Panoply

Coding solutions

Code-based solutions allow for automating data workflows by using custom scripts containing specific instructions for carrying out each step in the process. Anybody who can program in the chosen program language can run these scripts.

The advantages of these code-based solutions are clear — they allow ease of automation and make it possible to extract lots of data from spreadsheets or databases in a short amount of time. Most importantly, though, these files can be easily added to any source and version control, and their code is open-sourced so you can go through the code or add an integration yourself.

There are certain disadvantages to this tool. The learning curve for a code-based tool is usually very steep, and one must have a deep understanding of the coding language to use the application effectively.

If you are an organization with a large team, you can choose to set up your own infrastructure, and for those with smaller teams, you can choose to get the cloud-hosted platform (for fees). There are also fewer of these tools out there, so if you are not as happy with them, there are limited options.

Examples: Airflow, Dagster, Prefect

How do I decide what tool would be most beneficial to my team?

Many tools can be used in a company. When it comes to deciding which tool is best, four key questions should be taken into account:

a)?What is the type of work your team does? What are the use cases?

Sample:?Our team provides a data lake where customers can store a large amount of data. The data we process is how customers use the system and hardware logs.

b)?How much data do you need to process? Can the tool handle this size?

Sample:?We process about 210 GB in one day. In addition, we need to process the last six months from a data warehouse and then process the previous 2 years from another database.

c)?Which integrations do you need?

Sample:?We need AWS S3 integrations. Tools without this integration can be ruled out.

d)?What kind of skills does your team have?

Sample:?Most of our employees are skilled in data processing, especially in converting the data from one format to another. Most either use tools like Python or R.

e)?Do you have the bandwidth in your team to set up the infrastructure, or do you need to have the setup be done for you?

Sample:?We currently are a team of two, so we need something we can easily set up and develop. Obviously, price is important, but we do not mind paying extra for a product that is easy to set up.

Data flow automation tools that do the job

Data flow automation tools are integral to any business data management strategy. These tools are designed to make working with large amounts of data easier while ensuring that the data is consistent, accessible, and accurate. All you need to do is choose any of these tools, configure them according to your specific needs, and get started.

Non-coding-based solutions

Now we will talk about some common options for low-code or no-code solutions.

Fivetran

Fivetran delivers connectors that dynamically adapt to new schemas and APIs, allowing you to reliably access data. Fivetran keeps data up to date so you can use the freshest figures in your analysis. It helps ensure accuracy — whether you want to merge data, plan, or build forecasts.

Fivetran is an excellent solution because it allows you to easily and quickly transfer your data. This solution can work with most databases you are already using, so no matter what changes need to be made, you will be able to make them, saving you valuable time.

The only disadvantage I can see of Fivetran is its lack of support for specific integrations and some more advanced features, which are only available with specific databases.

Talend

The user-friendly and open-source product helps create integrations in a few clicks without writing custom code.

We can quickly get data from other sources, store them in relational databases, and then make changes to the produced code and store them as part of a version or source control.

Talend is not great with scalable processing management since it relies heavily on manual intervention. Talend misses a robust data quality component and can be difficult to debug because there are minimal logging features and only a tiny community to ask for help.

The produced code will be in Java, which can be a problem if you do not have anybody who codes in Java.

Xplenty

Xplenty is a cloud-based ETL solution and low-code data integration platform with a user-friendly drag-and-drop interface.

Xplenty makes it easy to connect to various data sources with all of the features ETL users have grown to expect.

Fixing their error messages and running more integrations would improve the product.

Coding-based solutions

I will now discuss some common solutions that require coding, including Airflow, Dagster, and Prefect.

Apache Airflow

Apache Airflow is a system to programmatically author, schedule, and monitor data processing workflows. It was designed to automate data processing workflows and make them scalable as data grows. Airflow can be used for ETL, data validation and quality assurance, machine learning pipelines, or any other type of task that can be broken down into a series of steps with dependencies between them.

Airflow provides the ability to author workflows as directed acyclic graphs (DAGs) of tasks. The user describes each task as a sequence of Python functions that produce output values, called tuples. Tasks can also have side effects, such as updating a database or sending an email.

Airflow is a project with a decade-long track record of success that is openly accessible to all and used by thousands of companies is a project worth considering. Airflow has a massive following on Stack Overflow and at other forums, so there is a lot of community support you can tap into.

Airflow has a ton of integrations called providers and makes it easy to create pipelines with your existing data tools.

Unfortunately, there are a handful of disadvantages. The local development and testing is a hassle to set up. They did add XCom to allow for data movement between tasks, but it is not a native solution as some other coding-based tools offer. And running multiple DAGs at the same schedule is not possible. Lastly, the Airflow’s UI is not as intuitive as one would hope and does not have an easy interface to create dynamic and parameterized workflows.

Dagster

Dagster is an advanced platform for data engineering. It has been designed with all aspects of the full development lifecycle from development to deployment, monitoring, and observability.

Dagster is powerful and flexible; you can fiddle with the settings, manually run some jobs, schedule whole DAGs and even adjust the behavior of individual jobs, e.g., you can provide different run-time configurations on weekends and weekdays. Dagster also offers an easy-to-use interface that lets you define a graph and parametrize the graph to adjust the configurations, resources, hooks, and executors to suit your needs.

Most of the product, including the cloud-hosted option, is still very new product. The community is still relatively small.

Prefect

A negative engineering philosophy drives Prefect to make its platform as simple as possible for users to build and deploy their code in a distributed pipeline, with the scheduling and orchestration engine as a key component.

There are a ton of advantages to using Prefect. Let’s go through them. A flow can be run anytime, as workflows are standalone objects. Prefect can run these DAGs and tasks extremely fast by taking advantage of tools like Dask. Perfect offers the ability to cache and persist inputs and outputs for large files and expensive operations, improving development time when debugging.

Prefect provides an easy interface for setting up a workflow that can be adjusted by its parameters in a top-level GUI or within code.

The main hurdle with Prefect is learning how to set up the framework and take advantage of the features. Prefect itself is also a relatively new product but more established than Dagster.

Reach out if you are looking to:

• Reduce ingestion time (data onboarding) by up to 80%
• Reduce cost of professional services and manpower by up to 50%
• Eliminate redundant and costly products and services that only offer partial solutions
• Utilize ALL data in decision making process
• Complete picture of entire digital footprint
• Single Pane of Glass