Data Orchestration vs. ETL: The Complete Breakdown

Data Orchestration vs. ETL: What's the Difference?

Data analytics and business intelligence are crucial for making informed decisions in today's data-driven world. However, accessing the right data can be a challenge, particularly when it's stored in silos. These silos can prevent data from being transported from one location to another, hindering effective data migration.

To solve this problem, organizations are turning to data orchestration. This process automates the data flow and eliminates data silos, making it easier to extract and use the right data for analysis. While ETL processes also involve moving data from one source to another, they don't provide the same level of data orchestration and automation.

In this article, we'll take a closer look at data orchestration and ETL processes to understand how they differ and which one is best suited for your organization's needs. So, let's dive in and explore data orchestration and ETL.

Data Pipeline

Before diving into the comparison between Data Orchestration and ETL, it's important to first understand the role of data pipelines in data management. Data pipelines are created to move data from one location to another. They are composed of various sources, including warehouse solutions, processes, and application components, among others.

Data pipelines collect, store, and process data for business purposes. Data within the pipeline is sourced from various sources, such as data warehouses and data lakes, and undergoes processes, such as ETL or ELT, to enhance the data's quality before loading it into the final destination, such as data analytical tools.

The processes within a data pipeline have interdependencies. Therefore, a centralized administration is necessary to coordinate dependencies, execute tasks in order, and identify and solve any errors. A system that is aware of the data's state at any given time and alerts users to any errors is also necessary to identify and fix issues. To obtain such a system, the entire data pipeline must be automated, which is where data orchestration comes into the picture.

Data pipelines can be used for the following use cases.

1. To give predictions at the end of data analysis

2. To create reports with real-time data

3. To enable metric updates

4. To migrate, process, and store data

Data Orchestration

Data orchestration is a crucial component of the data journey for any business. This journey involves a set of workflows, including data collection from various sources, transforming data for use in analysis and products, creating reports, and loading the data into a centralized warehouse. Data orchestration automates these workflows and organizes data to enhance the process of business decision-making. When all the workflows of a data pipeline are orchestrated, it is known as data pipeline orchestration.

In today's world of Big Data, organizations often store their data sets in multiple locations. Data orchestration connects all these storage locations to create a centralized data storage and ingests data directly into analytics tools. Data pipelines typically use either ETL or ELT integration methods. Depending on the integration method, data is transformed and optimized either after loading it into the destination or right after collecting the data. As a result, orchestration is an automation process that requires no data engineers.

Phases of Data Orchestration

The whole orchestration process consists of four steps called preparation, transformation, cleaning, and synchronizing.

1. Preparation

This step ensures the integrity and accuracy of the data. Existing data is completed by adding third-party and label data with the respective designation.

2. Transformation

This step converts data into a standard format. For example, dates can be written in different formats, such as 20.02.2016 or 20022016. The transformation phase can convert these into one standard format.

3. Cleaning

This phase identifies corrupted, nan, duplicate, and outlier data, and then processes and corrects the data.

4. Synchronizing

This phase continuously updates the data as it moves from one source to another destination. It ensures that data consistency is maintained during orchestration.

The use of Data Orchestration

Data orchestration offers several benefits that organizations can leverage to improve their data management processes.

1. Scalability

Data orchestration allows organizations to scale their data sets by breaking down silos and acquiring data from various sources. This helps to increase profits and reduce costs.

*2.  *Visibility and Data Quality

The orchestration process streamlines each step while catching errors. This allows for data monitoring and ensuring data quality.

3. Data Governance

Data flow is structured in data orchestration, and data from multiple sources is well-monitored according to data governance standards.

4. Real-time Data analysis

Data collected in data orchestration is directly available for use in data analytics tools. As the pipeline is automated, data is accessible as required with zero errors.

Data Orchestration Tools

Data orchestration platforms and tools use automation to perform data-driven processes efficiently. These tools aid in building data pipelines that merge source data, transform it, and ingest it to activate other applications. These tools are built according to the concept of Direct Acyclic Graphs (DAGs), which help break down silos. This concept identifies tasks as nodes of the graph and dependencies as the edges of the graph. With all tasks and dependencies captured properly, the system executes respective tasks more efficiently while logging and debugging errors.

These tools possess the following characteristics.

• Automation of the entire data movement process, which is easy to use and has a set of rules for extracting, transforming, and loading data.

• A visual drag-and-drop interface.

• Support for complex data management, such as complex computations and integrations.

There are several open-source software platforms that execute data orchestration. These platforms do not function as data storage but combine data across all data storage and use data analysis tools to access data.

1. Apache Airflow

Airflow pipelines are defined using Python code, allowing for dynamic pipeline generation. It enables building workflows that connect with Google Cloud Platform, AWS, Microsoft, and more. Airflow helps automate, schedule, and monitor workflows.

2. Shipyard

Shipyard connects your data tools, moves data between them, and alerts you if there are issues. It's a fully-hosted solution designed for data professionals of all technical backgrounds with a focus on low-code templates powered by open-source Python.

3. Metaflow

Metaflow is also built using the Python framework, increasing the productivity of data teams by building and managing real-time data. Metaflow is commonly used in developing and deploying machine learning and AI applications.

4. Prefect

This platform is capable of orchestrating a Python function by transforming it into a unit of work. It builds strong and dynamic workflows that can recover from unexpected changes.

5. Stitch

Stitch specifies when data replication needs to happen and identifies and resolves any errors that arise in data pipelines while monitoring data replication progress.

ETL Pipeline

A data pipeline that follows the ETL process to migrate data is known as an ETL pipeline. Data inside the pipeline is collected from various resources and transformed into a standard data format before being stored in a destination, which could be an existing data storage such as a data warehouse, relational database, or cloud-hosted database. ETL pipelines have several use cases:

1. Creating a centralized data warehouse to store all data distributed across multiple sources in an organization.

2. Transforming and transferring data from one source to another.

3. Enhancing the CRM system by adding third-party data.

4. Providing access to structured and transformed data for pre-defined analytics use cases, creating stable datasets for data analytics tools.

An ETL pipeline ensures data accuracy and stores it in a centralized location, enabling access for all users. Data analysts, data scientists, business analysts, and other professionals can then use the stored data in their use cases, allowing them to focus on business work instead of developing data analytics tools.

ETL (Extraction, Transform, Load)

The ETL process is responsible for extracting data from various sources. It then transforms this data into a standard format that facilitates data integration workflows for business intelligence needs and data analytics. Finally, the transformed data is transported to a final destination.

Here are the components of an ETL process.

1. Extract

This step aims to extract/pull structured and unstructured data from various sources into a single data repository. These sources can be other data warehouses, data lakes, CSV files, API endpoints, existing databases, CRM systems, etc. Currently, the rise of SaaS applications has resulted in the majority of data extraction occurring from an application's storage through APIs.

2. Transform

The collected data is transformed into a standard format using scripting languages and SQL procedures to make it suitable for data analysis use cases. Data is also cleansed to remove inconsistency and inaccuracy, and third-party data is added to the existing data. This step ensures data integrity.

3. Load

The transformed data is stored in a database in this step. The database can be a relational database, a data warehouse, a NoSql database, or a product that requires real-time data ingestion, among others. Typically, the loading of data is performed in batches when traffic is low at the warehouse and data source.

ELT (Extraction, Load, Transform)

ELT is another integration method, similar to ETL, that moves data from one source to another. However, ELT loads the data to the destination before performing any transformations. ELT is a suitable choice when organizations need to load data to one location and perform modifications or transformations. Although ETL and ELT perform the same task, they are used to fulfill different data needs.

ETL is the preferred integration method when a company has a specific data format in mind that needs to be used within the application and business processes. In this situation, ETL is used to transform the data before it is loaded into the data warehouse. This ensures that the data is of high quality before being stored in the warehouse or data lake.
ELT is well-suited for cloud-based data warehouses provided by platforms such as BigQuery, Snowflake, and Amazon Redshift, which offer scalable processing capabilities. These platforms can handle large amounts of data and are capable of real-time processing, making ELT a perfect choice for organizations that need to process and analyze data quickly.

Uses of ETL

Let's see some of the uses of ETL.

1. Clear Information

During the data transformation process, data is cleaned. Therefore, data analysts are able to work with clear and accurate data.

2. Complete Information

ETL gathers data from various sources and combines it before loading it into the data warehouse. This ensures that all data is present and that the information is complete.

3. Quality of Information

The extraction process validates data, while the transformation process eliminates errors, resulting in high-quality data that can be used for analysis and machine learning models.

4. Information Velocity

The ETL pipeline is triggered when new data arrives at the sources or when existing data is updated, allowing users to make faster business decisions.

ETL Tools

ETL orchestration tools are the best option to automate data integration. As described above it automates the whole process of data extraction, transformation and data loading. The followings are some common ETL tools used in the industry.

1. Portable

Portable is a cloud-based ETL tool designed for creating data pipelines without needing to write any code. With over 500 ETL connectors available, Portable supports even the hard-to-find connectors and allows for lightning-fast custom development. If the required connector is not available, Portable offers to build it on-demand. The Portable team is available to resolve issues, so users can focus on their work without worrying about any interruptions. Portable's pricing model is straightforward, with a flat rate per data flow that includes unlimited data volumes, eliminating the need for complex pricing structures.

2. Datarios

Datarios is a powerful data extraction and integration tool that specializes in the extraction phase of the ETL process. Its user-friendly interface takes data source information and connection details as inputs to quickly and easily extract data.

3. Talend

Talend is a comprehensive ETL tool that has a wide range of plugins to integrate with Big Data ecosystems. In addition to integration, it also helps with data preparation and data quality. Talend has a user-friendly interface, making it easy for businesses to quickly set up and manage their ETL workflows.

4. Pentaho

Pentaho is a versatile open-source data integration tool that can handle data mining, reporting, data analysis, dashboard creation, customized ETL, and more. It supports real-time analysis, which is critical for businesses that require quick decision-making based on up-to-date data. Pentaho's intuitive interface makes it easy for businesses to customize their ETL workflows to suit their specific needs.

Data Orchestration in ETL

Data orchestration is an essential part of the traditional ETL process, in which data engineers transform data into a standard format for analysis. However, this process can be complex and time-consuming, as each data source may require specific implementations. To simplify this process, orchestration tools have been developed to automate tasks and streamline data loading. These tools are used in ETL pipelines to improve efficiency and enable on-premises data processing.

Comparison: Data pipeline and ETL pipeline.

Even though an ETL pipeline is a type of data pipeline, the two pipelines have different characteristics, and ETL alone cannot support real-time data applications. The following are the significant characteristics that differentiate a general data pipeline from an ETL pipeline.

• An ETL pipeline always loads data to another database or a data warehouse, while a data pipeline ingests data into analytics tools instead of using a warehouse.

• Data pipelines can either transform data or not transform it at all, but ETL pipelines always transform data before loading it into the target destination.

• ETL pipelines load data in batches on a regular schedule, whereas data pipelines support real-time processing with streaming computation and allow data to update continuously.

• ETL pipelines end the process after loading the data, whereas data pipelines continue to update and process.