Get control over your data pipelines with data orchestration

Enterprises are tapping and leveraging big data to get ahead of the competition. As Peter Sondergaard, ex-Executive Vice President at Gartner said:

“Information is the oil of the 21st century, and analytics is the combustion engine”

The problem with the combustion engine is that it does not scale well. 

As companies grow, the data platforms they previously relied on for analytics start to break apart. 

The increased number and diversity of incoming data sources, the complexity of preprocessing and postprocessing pipelines applied on data to make it analysis-ready, and the sheer multitude of upkeep and maintenance needed to keep the engines running slows down the data-driven decisioning of companies as they grow.

What can mitigate these growing pains? Data orchestration.

What is data orchestration?

Data orchestration is the automation of end-to-end data processes across the entire data ecosystem. 

That’s a mouthful. Let’s dive deeper to unveil the meaning of data orchestration. 

To understand data orchestration, we need to understand the workflows that lead to analytics data, that is, data that is ready to be ingested by BI tools or taken over by data scientists and fed to machine learning algorithms. 

Data flows from its raw state to its final state via ETL or ELT workflows. The ETL workflow has three main components:

1. Data Extraction. Data is collected from raw sources. This might be done directly, by querying your CRM/ERP databases, or via calls to 3rd Party Apps’ APIs, such as Facebook Ads API or Salesforce API. 
2. Data Transformation. Data is cleaned and aggregated. Outliers are removed, business interpretations are added (for example, tagging data as belonging to new customers vs returning customers, …), metrics are computed, and values are aggregated (for example by time: daily numbers, monthly, quarterly, yearly, …). 
3. Data Loading. Data is stored into storage systems such as databases, data warehouses, or data lakes. From here, data access is granted to BI tools and data scientists, who analyze data.
   

Each of the three components has devoted scripts (or workloads, or jobs, or processes) that do that one job extremely well. 

For example, you might have a SQL-based script that triggers whenever a new incoming data is being extracted, and the script automatically removes outliers in the transformation layer, before data is loaded into your data warehouse.

Data orchestration is the automation of the components in the ETL pipelines and their workflows. There are two possible ways of implementing data orchestration:

1. Automatic component orchestration. This type of data orchestration automatically triggers a workload. For example, automatic data extraction whenever new data is being generated at the source or automatic data transformation whenever new input data is being collected.
2. Automatic data pipeline orchestration. This type of data orchestration automates end-to-end data pipelines. For example, each day at 23:00, a data orchestration workflow would trigger the entire advertising ETL: data is extracted from advertising APIs (Google Ads API, Facebook ads API, Linkedin Ads API, …), data is then transformed (cleaned and aggregated) and afterwards, it is automatically saved to your data warehouse.
   

Enterprises are turning to data orchestration to manage their specific scaling needs.

Why is data orchestration needed?

Modern enterprises experience several pains within their data ecosystem:

1. Data silos. Enterprise data is produced and stored in data silos - across different geographical regions on the globe and a multitude of sales, marketing, procurement, financial, and other software. These data need to be integrated and synced before a holistic picture can be made from the underlying data.
2. Variability of data. Different sources of data need different treatment. Each API has its idiosyncrasies that push data engineers to write devoted scripts.
3. Maintenance. As an enterprise grows, so does the need to upgrade, troubleshoot, and tune the system. Each breaking component has downstream effects, so data engineers need to move fast to correct extraction, transformation, and loading issues.
   

Every single aspect of the modern enterprise data ecosystem becomes progressively harder and more painful as companies scale: new silos are created, data changes at its source with every new input addition, and maintenance becomes harder as new software, tools, and transformations are being added to the system. 

Deploying data orchestrations eases the growing pains. But it also brings additional advantages.

What are the benefits of data orchestration?

There are multiple benefits to deploying data orchestration:

1. Scalability. Data orchestration automates the synchronization across data silos, handling changing input data, and lowers maintenance costs. This makes scaling easier.
2. Monitoring. Automated data orchestration is implemented with monitoring. This means it is easier and faster to spot when something goes wrong. Note the “when” and not the “if” in the previous sentence. All data pipelines break. Automating end-to-end data pipelines and equipping them with alerts and monitoring, allows data engineers to recognize, identify, and correct issues faster than when the same pipelines are written with custom scripts and varying monitoring standards.
3. Data governance. Deploying data orchestration as its layer upon your data platform allows you to envision all data pipeline workloads. This is crucial for data governance, where you need to have the ability to track customer data as it is collected and changed throughout your system.
4. Real-time data. Running automatic data orchestration keeps your data fresher. In fact, with properly deployed data orchestration you can have near real-time data since automatic data extraction can be triggered via data orchestration whenever new data is produced. 
5. Streamlined time to insights. Deploying end-to-end data orchestration means you automate end-to-end the production of important dashboards. This means your time to insights is automated and can be further optimized within a single place.
   

The multiple benefits that data orchestration unlocks are highly coupled with the implementation of data orchestration. 

So, how do enterprises deploy their data orchestration? By relying on tried-and-tested data orchestration tools.

The best data orchestration tools

There are two types of tools you need to consider when building your data orchestration: open-source solutions and enterprise-ready commercial tools.

Open-source data orchestration tools

Open-source data orchestration tools rely on the concept of workflows-as-code. That is, end-to-end data orchestrations are written as code, aka a Python script for instance. 

This pattern is similar to the custom script writing of the “before data orchestration era” with one crucial difference: data orchestration is an independent solution, with its monitoring, dashboarding, and holistic ecosystem.

Among the best open-source data orchestration tools we can find:

• Luigi
• Apache Airflow
• Dagster
• Prefect
• … and others
  

There are several benefits to running data orchestration on open-source tools:

• The software is free 
• Open-source software usually has a devoted community that can act as support when things go south
• You can customize the software yourself
  

But open-source has also issues:

• No devoted support
• Deployment and maintenance costs (e.g., migrating from one version to another has downstream consequences on your operations)
• Need for specialized skills for using software

Enterprise-ready data orchestration tools

Enterprise-ready data orchestration tools are a category of software, where the software is offered as a service (SaaS model). 

Among notable examples of commercial data orchestration tools are:

• Keboola
• AWS Glue
• Google Cloud Composer
• … and others

Saas has multiple benefits:

• Devoted support
• Deployment, upgrades, and maintenance are taken care of by the vendor
• Clear and up-to-date documentation
• Higher availability and quicker bug fixes
  

There are also downsides with commercial software:

• Price can be negligible or it can be steep
• Sometimes even commercial software necessitates high technical skills
  

Why is Keboola the right data orchestration tool?

There are three main reasons why Keboola beats the other tools:

1. Its features are enterprise-grade. Keboola was built for enterprises and is maintained by a team of highly specialized engineers. The customer reviews speak for themselves. 
2. Its pricing is startup-friendly. You can try out and even use Keboola for free. Each month users get 300 free minutes to run their data operations. If more is needed, Keboola offers a generous pay-as-you-go plan that scales with your needs. 
3. Its platform is user-friendly. Keboola is built to democratize data access. With its intuitive UI, non-technical people can build their data orchestration pipelines without the need for specialized cloud engineering skills. But if you want to dig deeper, Keboola is built for developers as well. From open-sourcing access to its components to extensive developer documentation, Keboola can be used by technical and non-technical profiles alike. 
   

Feel free to take Keboola for a free spin or reach out to us if you have any questions.