An organization’s data needs can be met from a variety of sources. The sources of data might include historical data, batch data, and streaming data. This data can be stored in different databases and in different forms. This creates silos, which act as barriers for teams to collaborate, leading to poor data practices. So, it is imperative to remove silos and make the data more accessible and available across the organization.
Before the advent of big data, data collection and transformation jobs were done manually. Developers used to run scripts and monitor the results of each job manually. But as the amount of data increased by multiple folds, the manual option became out of the question. So, developers started to use cron jobs for scheduling tasks. But after a point, even cron jobs weren’t sufficient to handle the scale. Monitoring, handling dependencies, and rerunning failed jobs out of 1000s of jobs was tedious. A lot of time and effort was required to log details such as task runtime, the status of the job, dependencies required, and scheduling details. Data engineers were in need of a system that offered a holistic view of all these tasks in a centralized location.
These two wants led to the rise of data orchestration frameworks, which enabled developers to programmatically create, monitor, schedule, and alert pipelines with ease. With a data orchestration framework, we can combine data from multiple sources, transform them to a fixed standard and store them in a centralized location so that multiple teams can use this data to power their applications.
Data orchestration tools enforce the relationship and dependency between the tasks. Multiple tasks can be run simultaneously in this approach. This is done through the creation of a Directed Acyclic Graph or DAG, which is a series of tasks that can be triggered manually or through scheduling. A typical DAG involves waiting for data, collecting the data, sending the data to a different source for transformation, monitoring the state of the task, getting the resultant data after preprocessing and storing it in a centralized source.
There are many popular data orchestration tools available in the market. A few of these are:
- Apache Airflow: Developed by AirBnB, it is one of the most popular data orchestration tools available. It uses a web interface and command-line utilities to author workflows as directed acyclic graphs.
- AWS Data Pipeline: Developed by Amazon Web Services, it helps in copying and transforming data that is stored on AWS.
- Google Cloud Dataflow: Developed by Google, it helps in building and managing data pipelines. It can process both batch and stream data.
- Microsoft Azure Data Factory: Developed by Microsoft, it is a cloud-based data integration service that helps in creating data-driven workflows for orchestrating and automating data movement and transformation.
- Luigi: Developed by Spotify, it uses Python to build complex pipelines and handle dependencies programmatically.
These orchestration tools offer different features, and it is essential to choose the right tool based on the use case. But one of the most important factors while choosing an orchestration tool is its integration with other tools and technologies in the organization’s data ecosystem. Another factor is the ability to monitor and track the progress of each task in the DAG. And finally, ease of use and debugging capabilities are some other factors that should be considered before choosing an orchestration tool.
Advantages of Data Orchestration
- Cost and time-efficiency: Managing thousands of pipelines is a very intensive operation, both in terms of time and manpower. Developers have to spend valuable time going through the logs, which could be better spent elsewhere. Since orchestration takes care of repetitive tasks, developers can focus on intuitive problem-solving. As there is very little to no human intervention, the possibility of human error decreases as well. This leads to improvement in overall productivity.
- Breaking silos: In addition to getting the right data we must ensure that we get the data at the right time. Having data silos within an organization delays the process of getting data at the right time. Due to the centralized nature of data orchestration, the concept of silos is broken. This in turn enables the organization to make informed decisions in a timely manner before it's too late.
- Improved data quality: An organization might get its data from data lakes, data warehouses, blobs, message queues, relational databases, NoSQL databases, streaming services, etc. Every database will have its own format. Some of the data may be structured, some might be semi-structured and the rest will have no structure at all. Even for structured data, each business follows its own convention.
For example, let's take the state of New York. Some of the different notations are NY, Newyork, NYC, New York City, and NYS. This leads to extreme disparities in the data. So we have to convert the data based on a gold standard. This makes data accessible across the organization. As data orchestration involves defining streamlined workflows, which take care of the transformations, it helps in maintaining the quality of data.
- Monitoring - Monitoring thousands of pipelines is extremely tedious. There are a lot of parameters and dependencies that need to be monitored. Since the orchestration tool provides centralized access to monitor all the workflows we can easily identify the tasks that require handling.
- Easier migration - Due to the rise of different cloud service providers, a lot of companies are looking to migrate their on-premises data to cloud data warehouses. Migration should be done efficiently without the loss of data. This is a complex process in itself, but if we are required to perform incremental migration then the complexity increases exponentially. As data orchestration tools have efficient checks and connect to multiple services, they make migration easier.
Challenges of Data Orchestration
- Integration capabilities: The data orchestration tool must be able to connect with a lot of services. It must have built-in plugins to connect to all the standard databases and also support custom plugins to connect to newer ones. This is extremely important because the orchestration tool must connect with a plethora of sources to get and send data. The number of sources an orchestration tool can support determines its reach.
- Ease of setting up: The ease of setting up and running an orchestration tool is another important challenge. We have to set up the storage and compute infrastructure for the orchestration tool to run. Additionally, we may have to configure the network as well. Setting up data orchestration tools can be complex. The orchestration tool must also have less maintenance overhead.
- Regulations: GDPR and HIPAA have rigorous requirements for the security, use of private data, encryption, and storage location. In a data orchestration system, the data keeps moving from one source to another so it will be a challenge to comply with these regulations.