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Introduction to Data Orchestration

According to a report by Gartner, more than 87% of organisations are not capable of utilising data for business intelligence and data analytics. The one reason behind this can be the inability to extract the right data from the data silos. Since these silos are data tables and restricts data to be migrated to other locations, data migration becomes a really complex task.

Also, the organisations have many more operations to handle, they lack in data governance. There can be various scenarios which restrict companies or organisations to extract and analyse their data. Data orchestration is one of the solutions that helps in the process of taking siloed data out from multiple data storages or data locations, combining and organising it, and automating data flow to data analysis tools. In this article, we will have an introduction to data orchestration. The points to be discussed in the article are listed below.

Table of content

  • What is data orchestration?
  • Need for data orchestration
  • Parts of data orchestration
  • Challenges being overcome by data orchestration
  • Benefits of data orchestration

What is Data Orchestration?

Data orchestration is a process of automating data flow right from bringing all the data together to preparing and making it available for data analysis. In a primary term, we can say that data orchestration is a process of breaking down the high data storage in managed ways. The main motive behind the data orchestration should be to automate and streamline data to enhance the company’s data-driven decision-making.

Some software/platforms like Apache Airflow, Metaflow, K2view, and Prefect help execute data orchestration by connecting storage systems, and data analysis tools can easily access the data. However, these software or platforms are completely new technology and don’t act like a data storage system.

Talking about the traditional ways, they may involve the following time-intensive steps of preparing data from big storage:

  1. Use custom scripts to extract data in CSV, Excel, JSON or database formats.
  2. Validation and cleaning of the data.
  3. Data conversion is required form.
  4. Load into the target destination.

Data orchestration is a way to exclude these time-intensive processes from the path of data preparation.

Need for Data Orchestration

The above-given data processing steps may be exemplary when the number of data systems is low. Still, when it comes to big businesses with multiple data systems, data orchestration becomes ideal. Using this technology, we don’t need to combine multiple data systems together. Instead, data orchestration provides access to the required data in the required format and at the time necessary.

Using data orchestration, data available across multiple data sources can be accessed easily and quickly. It is also better because we don’t require any central data storage to handle large amounts of data.

We may think of data orchestration as ETL(extract, transform, load process) but ETL has a specific written script to follow and process the data. Data orchestration is more on automation of the steps of ETL. There are a few parts of data orchestration. Let’s take a look at them.

Parts of Data Orchestration

Data orchestration can be segregated into 4 parts:

  1. Preparation: This part includes the process of checking the integrity and correctness of data. Also labelling the data, providing designation to data and including third-party data with existing data can be completed in this part.
  2. Transformation: this part includes the data conversion and formatting. For example names of people can be written in different formats like [surname] [name] or [name] [surname]. So here, we are required to make all these in the same format.
  3. Cleaning: This part includes data cleaning processes, identification and correcting the corrupt, inaccurate, nan, duplicated and outlier data.
  4. Synchronising: This part includes continuous updations in the way of data from data source to destinations so that consistency can be maintained. This part is similar to your photos, videos and contacts synced on all your devices using google drive.

Challenges being overcome by data orchestration

Data orchestration came into the picture when handling big data became more complex. There are various challenges people are facing while handling big data using ETL. These challenges include:

  • Disparate data sources: In large organisations, when data comes from multiple data sources, it doesn’t come in the analysis-ready situation. Here, data orchestration plays an essential role by automating the data maintenance and quality checking process.
  • Data Silos: There are higher chances of getting required data siloed in such a location or organisation from where accessing data for the subsequent processes is complex. Here orchestration helps in breaking down the silos and makes data more accessible. This breakdown of silos is done with the help of DAG(direct acyclic graph) that represents the relationship between tasks and data systems.
  • Data validation: As data literate, we know that data cleaning and organising are time-consuming processes. Data orchestration helps avoid such time consumption when data is required for analysis.

Benefits of Data Orchestration

Data orchestration can provide the following benefits:

  • Scalability: Being a cost-effective way to automate data synchronisation across data silos, Data Orchestration helps organisations to scale data use.
  • Monitoring: Data orchestration enables alerts and monitoring systems within it and that helps data engineers to monitor data flow across the systems where ETL utilises complex scripting and disparate monitoring standards.
  • Data governance: Orchestration helps users monitor customer data because the data gets collected throughout a system. For example, handling data of different geographical regions with different privacy and security rules and regulation.
  • Real-Time information analysis: One of the major benefits of data orchestration is that it allows real-time data analysis. Also till now, it is the quickest way to extract and process data.

Final words

In this blog, we have seen how data orchestration is making data more useful in accurate, efficient and quick way. Because of data orchestration, it has become common to leave our data fragmented and in silos. Along with this we went through its parts and looked at how every part stands and works. Since this technology has come in boom around the year 2010, it is in a developing phase where we can observe changes frequently. It won’t be surprising for us to see ETL be replaced by data orchestration in future. So keeping track of the development of such technology becomes very necessary for those who are dependent on data.