The What, Why, And When of Data Orchestration
Everything you need to know about data orchestration and popular frameworks in 2021.
When most people hear the word orchestration, they envision an orchestra playing a symphony. At the front of every orchestra stands a conductor with his waving arms who prompts all the instruments in the orchestra so that they’re both timed and synced. In the end, the audience can enjoy a unified vision of the music aligned with the right level of intensity.
Why the metaphor — you ask. Well, turning data into actual, valuable information needs a conductor as well. In the past, most data ingestion was done as part of a scheduled batch job overnight but the cloud has changed all that. Over the past few years, numerous data orchestration frameworks have seen the light of the day and become essential components of the modern data stack. But before diving into the orchestration solutions, let’s get under the covers of data orchestration and its importance for data analysis.
What Is Data Orchestration Anyway?
Although the term is a relative newcomer, it has been gaining momentum over the past few years. And here are some nice reasons for that. Nowadays, the growth of data remains unprecedented, coming from a myriad of sources. The increasingly complex movement of data across a wide variety of ecosystems is making management very difficult, and this anti-trend will continue in the future. Thus, Gartner’s research demonstrates that by 2025, AI requirements will have significantly increased the volume of data for a staggering 75% of enterprises. Data orchestration is how organizations can stay on top.
Besides, data is as important an asset as capital assets and intellectual property. And the more data there is, the more difficult it is to manage. That’s why enterprises need new ways to move and orchestrate data.
Essentially, data orchestration refers to eliminating data silos so that your data isn’t all over the place and can be entered on demand. In reality, if an organization handled its data well enough, it wouldn’t need data orchestration. But this is rarely possible due to the ever-evolving technologies and the growing data ocean.
The notion under question usually describes the set of technologies that automate data-driven processes, virtualize all the data, and present the data via standardized APIs with a global namespace to data-driven applications. It also presupposes the centralized control of processes that manage data across disparate systems, data centers, or data lakes. As a result, IT teams are able to create and automate full-scope processes that include data, files, and dependencies from across the organization, without having to write custom scripts.
Data orchestration is a safe bet for most companies with various data flows because it doesn’t need any huge migrations for your data, which can often introduce another data silo. Among other things, data orchestration also facilitates compliance with data privacy laws, eliminates data bottlenecks, and amplifies data governance.
Data Orchestration Frameworks — A Missing
Piece in the Data World
Although businesses continue to invest in data science and AI technologies, they still struggle to understand the incoming value. Also, most companies may have the fine-tuned methodology in place, but not the tools or the technology for paying due diligence to data. As a result, businesses end up delegating the whole data cycle to manual processes and generating redundancies. Data orchestration frameworks help to glue it all together, by creating an ideal data science environment equipped with automated tools. — either open-source or proprietary thus making the entire process truly automated. Some of the functionality provided by orchestration frameworks are:
- Job Scheduling
- Dependency management
- Error management and retries
- Job parametrization
- SLAs tracking, alerting, and notification
- Data storage for metadata, and others.
Among the most popular frameworks that manage dependencies are Apache Airflow, Ozzie, and Lugie. Let’s have a closer look at each of them and find out what makes them so favored.
Apache Airflow
Apache Airflow is a multi-functional workflow manager that has made it to the toolbox of any knowledgeable data engineer. If you look at open vacancies for data engineer positions, you will often find Airflow experience as one of the requirements for the position.
Developed in 2014, it is an open-source tool that allows you to develop, plan and monitor complex workflows. The main differentiator is that it uses the Python programming language to describe the processes. This feature provides you with a number of advantages for managing your project and development. Thus, it transforms your, let’s say, ETL project into a simple Python project, so you can turn and twist it however you want, based on the infrastructure, team size, and other requirements. From the technical point of view, it’s also simple. You can use PyCharm + Git, for example.
The main workflow entities in Apache Airflow include:
- Directional acyclic graphs (DAGs).
- Scheduler
- Operators
- Tasks
This framework can also orchestrate complex Machine Learning workflows. and it can be heavily customized with plugins. That is why it is favored by Data Engineers and Data Scientists alike.
Oozie
Apache Oozie is an orchestrator known for its tight integration with the Hadoop stack. It is included in the largest Hadoop distributions from Cloudera and Hortonworks. Essentially, Apache Oozie is a server-based workflow scheduler system to manage Apache Hadoop jobs.
Like Apache Livy, workflows in Oozie are represented as a DAG-chain (Directed Acyclic Graph). This framework supports running Hadoop MapReduce, Apache Hive, Pig, Sqoop, Spark, HDFS operations, UNIX Shell, SSH, and email tasks, and can be extended to support additional actions. Tasks can be set up to run regularly or once upon the occurrence of some event. Oozie itself is implemented as a Java web application that runs in a Java servlet container and is distributed under the Apache license.
Oozie looks terrifying at first because it is entirely powered by XML, which is hard to debug when an issue pops up. However, once you get to grips with it, it works wonders. Being very powerful for scheduling Hadoop jobs, it supports a great number of action nodes in pipelines and manages complex dependencies.
Another forte of the Oozie framework is that it is fully integrated with the Apache Hadoop stack and supports Hadoop jobs. It can also be used to schedule system-specific jobs, such as Java programs. Oozie allows Hadoop administrators to create complex data transformations that can combine the processing of a variety of individual tasks and even sub-threads of jobs. This functionality improves the controllability of complex tasks and makes it easier to repeat these tasks at predetermined intervals.
Luigi
Luigi is another orchestrator that uses Python to describe task graphs. It was initially created to run complex pipelines in the recommendation system based on Apache Hive, Spark, and other Big Data technologies. Luigi became available as an open-source project under the Apache 2.0 license in 2012.
This framework allows you to build complex pipelines of batch jobs and manage dependency resolution, workflow management, visualization, and much more. Luigi is also designed to manage workflows by rendering them as a DAG pipeline. This framework is easier to use than Airflow but has fewer features and more limitations.
However, as of the end of 2020, it is Apache AirFlow that is considered the leading DataOps tool for automated Big Data Pipeline orchestration. This is due to its focus on large production solutions and a number of other advantages.
What Orchestrator To Choose
The orchestration framework you choose will be mostly based on your goals compared to the infrastructure and complexity that each solution offers.
To narrow down your search, go over the following points:
- Do you need functionality to move and transform big data? Typically, this would involve several gigabytes to terabytes of data. If yes, select the options that are well-suited for big data.
- Do you require a managed service that can operate at the right scale? If yes, choose one of the cloud-based services that are not limited by the computing power of the local computer.
- Are some data sources hosted locally? If yes, select options that can work with cloud and local data sources or destinations.
- Is the source data placed in a BLOB object repository in the HDFS file system? If yes, select an option that supports Hive queries.
The Bottom Line
On the way to transforming into a truly data-driven digital business, companies face the challenge of managing an intricately intertwined fabric of data. And this is when orchestrators step into the spotlight. The main aim of any data orchestration platform is to shed light on messy data. Just like choir conductors, orchestrators allow each data point to sync with other information at a scale.
