We use data every day — in different forms — to make informed decisions. It could be through counting your steps on a fitness app or tracking the estimated delivery date of your package. In fact, the data volume from internet activity alone is expected to reach an estimated 180 zettabytes by 2025.
Companies use data the same way but on a larger scale. They collect information about their targeted audiences through different sources, such as websites, CRM, and social media. This data is then analyzed and shared across various teams, systems, external partners, and vendors.
With the large volumes of data they handle, organizations need a reliable automation tool to process and analyze the data before use. Data orchestration tools are one of the most important in this process of software procurement.
What is Data Orchestration and Data Pipelines
Data orchestration is an automated process of data pipeline workflow. To break it down, let’s understand what goes on in a data pipeline.
Data moves from its raw state to a final form within the pipeline through a series of ETL workflows. ETL stands for Extract-Transform-Load. The ETL process collects data from multiple sources (extracts), cleans and packages the data (transforms), and saves the data to a database or warehouse (loads) where it is ready to be analyzed. Before this, data engineers had to create, schedule, and manually monitor the progress of data pipelines. But with data orchestration, each step in the workflow is automated.
Data orchestration is collecting and organizing siloed data from multiple data storage points and making it accessible and prepared for data analysis tools. With this automation act, businesses can streamline data from numerous sources to make calculated decisions.
The data orchestration pipeline is a game-changer in the data technology environment. The increase in cloud adoption from today’s data-driven company culturehas pushed the need for companies to embrace data orchestration globally.
Why is Data Orchestration Important
Data orchestration is the solution to the time-consuming management of data, giving organizations a way to keep their stacks connected while data flows smoothly.
“Data orchestration provides the answer to making your data more useful and available. But ultimately, it goes beyond simple data management. In the end, orchestration is about using data to drive actions, to create real business value.”
As activities in an organization increase with the expansion of the customer base, it becomes challenging to cope with the high volume of data coming in. One example can be found in marketing. With the increased reliance on customer segmentation for successful campaigns, multiple sources of data can make it difficult to separate your prospects with speed and finesse.
Disparate data sources. Data orchestration automates the process of gathering and preparing data coming from multiple sources without introducing human error.
Breaks down silos. Many businesses have their data siloed, which can be a location, region, an organization, or a cloud application. Data orchestration breaks down these silos and makes the data accessible to the organization.
Enforces data governance. The data orchestration tool connects all your data systems across geographical regions with different rules and regulations regarding data privacy. It ensures that the data collected complies with GDPR, CCPA, etc., laws on ethical data gathering.
Gives faster insights. Automating each workflow stage in the data pipeline using data orchestration gives data engineers and analysts more time to draw and perform actionable insights, to enable data-based decision-making.
Provides real-time information. Data can be extracted and processed the moment it is created, giving room for real-time data analysis or data storage.
Scalability. Automation of the workflow helps organizations scale data use through synchronization across data silos.
Monitoring the workflow progress. With data orchestration, the data pipeline is equipped with alerts to identify and amend issues as quickly as they occur.
Best Tools Data Orchestration Tools
Data orchestration tools clean, sort, arrange and publish your data into a data store. When choosing marketing automation tools for your business, two main things come to mind: what they can do and how much they cost.
Let’s look at some of the best ETL tools for your business.
Shipyard is a modern data orchestration platform that helps data engineers connect and automate tools and build reliable data operations. It creates powerful data workflows that extract, transform, and load data from a data warehouse to other tools to automate business processes.
The tool connects data stacks with up to 50+ low-code integrations. It orchestrates work between multiple external systems like Lambda, Cloud Functions, DBT Cloud, and Zapier. With a few simple inputs from these integrations, you can build data pipelines that connect to your data stack in minutes.
Developed by Spotify, Luigi builds data pipelines in Python and handles dependency resolution, visualization, workflow management, failures, and command line integration. If you need an all-python tool that takes care of workflow management in batch processing, then Luigi is perfect for you.
It’s open source and used by famous companies like Stripe, Giphy, and Foursquare. Giphy says they love Luigi for “being a powerful, simple-to-use Python-based task orchestration framework”.
Some of its key features are:
Task-and-target semantics to define dependencies
Uses a single node for a directed graph and data-structure standard
Light-weight, therefore, requires less time for management
Allows users to define tasks, commands, and conditional paths
Data pipeline visualization
Luigi is an open-source tool, so it’s free.
3. Apache Airflow
If you’re looking to schedule automated workflows through the command line, look no further than Apache Airflow. It’s a free and open-source software tool that facilitates workflow development, scheduling, and monitoring.
Most users prefer Apache Airflow because of its open-source community and a large library of pre-built integrations to third-party data processing tools (Example: Apache Spark, Hadoop). The greater flexibility when building workflows is another reason why this is a customer favorite.
Some of its key features are:
Easy to use
Robust integrations with data cloud stacks like AWS, Microsoft Azure
Streamlines UI that monitors, schedules, and manages your workflows
Standard python features allow you to maintain total flexibility when building your workflows
Its latest version, Apache Airflow 2.0, has unique features like smart sensors, Full Rest API, Task Flow API, and some UI/UX improvements.
Keboola is a data orchestration tool built for enterprises and managed by a team of highly specialized engineers. It enables teams to focus on collaboration and get insights through automated workflows, collaborative workspaces, and secure experimentation.
The platform is user-friendly, so non-technical people can also easily build their data orchestration pipelines without the need for cloud engineering skills. It has a pay-as-you-go plan that scales with your needs and is integrated with the most commonly used tools.
Some of its key features are:
Runs transformations in Python, SQL, and R
No-code data pipeline automation
Offers various pre-built integrations
Data lineage and version control, so you don’t need to switch platforms as your data grows
Fivetran has an in-house orchestration system that powers the workflows required to extract and load data safely and efficiently. It enables data orchestration from a single platform with minimal configuration and code. Their easy-to-use platform keeps up with API changes and pulls fresh, rich data in minutes.
The tool is integrated with some of the best data source connectors, which analyze data immediately. Their pipelines automatically and continuously update, freeing you to focus on business insights instead of ETL.
Some of its key features are:
Integrated with DBT scheduling
Includes data lineage graphs to track how data moves and changes from connector to warehouse to BI tool
Supports event data flow data
Alerts and notifications for simplified troubleshooting
Fivetran has flexible price plans where you only pay for what you use:
Starter — $120 per month
Standard Select — $60 per month
Standard — $180 per month
Enterprise — $240 per month
Business Critical — Request a demo
A second-generation data orchestration tool, Dagster can detect and improve data awareness by anticipating the actions triggered by each data type. It aims to enhance data engineers’ and analysts’ development, testing, and overall collaboration experience. It can also accelerate development, scale your workload with flexible infrastructure, and understand the state of jobs and data with integrated observability.
Despite being a new addition to the market, many companies like VMware, Mapbox, and Doordash trust Dagster for their business’s productivity. Mapbox’s data software engineer, Ben Pleasonton says, “With Dagster, we’ve brought a core process that used to take days or weeks of developer time down to 1-2 hours.”
Some of its key features are:
Greater fluidity and easy to integrate
Various integration options with popular tools like DBT, Spark, Airflow, and Panda
Dagster is an open-source platform, so it’s free.
Companies are increasingly relying on the best AI marketing tools for a sustainable, forward-thinking business. Leveraging automation has helped them accelerate their business operations, and data orchestration tools specifically have provided them with greater insights to run their business better.
Choosing the right ETL tools for your business largely depends on your existing data infrastructure. While our top picks are some of the best in the world, ensure you research well and select the best one to help your business get the most out of its data.
A customer data platform (CDP) centralizes an organization’s customer data, providing a single 360-view of each consumer that engages with the company. Yet there are still data-related considerations that organizations have to make beyond what the CDP does.
“[CDPs] were designed to fill a need – to enable a marketer to easily get to the data they need to create their segmentation and then go on and mark it from that point,” said George Corugedo, CTO of data management company Redpoint Global, at The MarTech Conference. “But the issue is that CDPs really don’t take care of the quality aspects of the data.”
Maintaining data quality also impacts segmentation, campaigns and privacy compliance challenges for marketing teams that use this data.
The data in a CDP depends on the quality of where it came from. Therefore, an organization using a CDP must also consider the quality of the data sources and reference files used to build out the CDP.
“The inevitable question is going to be, how good is this data?” said Corugedo. “How much can I trust it to make a bold decision?”
This is something that has to be on every organization’s radar. For instance, when identity resolution is used, the issue depends on the quality of the third-party reference files. If they are provided by a telecommunications company or credit bureau as the data partner, those files might only be updated quarterly.
“It’s just not an optimal solution, but every single CDP on the market uses some form of reference file,” Corugedo stated.
It’s up to the data scientists and other team members working within the organization to own the accuracy of these data sources.
The quality of the data using specific reference files and sources will vary and will impact the confidence that marketers have in creating segments and using them when deploying campaigns.
Marketers have to make this decision at a granular level, based on the trustworthiness of data from a particular lineage.
“If they have a campaign that is reliant on suspect data, they can actually delay that campaign and say maybe we wait until that data gets refreshed,” said Corugedo.
Otherwise, marketers are just “spraying and praying.”
Using rules instead of lists
The advantage of having a CDP is unification of all data. But the data is being updated all the time. Instead of deploying campaigns based on a fixed list of customers, the use of rules to define segments allows marketers to update who they engage in the campaign.
“A list, as soon as it’s detached from the database, starts to decay because it doesn’t get any updates anymore,” Corugedo, adding that using lists takes longer to execute a campaign.
Lower quality from data that isn’t updated can have serious implications for healthcare and other industries, where accuracy is essential.
“Instead, rules are passed through the campaign just like they would be with a list, but those rules reevaluate every time there’s a decision point to make sure that only the qualified people get the particular content at that point,” Corugedo explained.
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Privacy and regulatory compliance
Maintaining data quality through a Redpoint Global dashboard, or a similar combination of tools and data personnel, will also help an organization manage privacy.
The crucial point is that people on the team know where the data came from and how it’s being used in campaigns. The stakes for sending out relevant messaging are high. Privacy and compliance issues raise the bar even higher.
If you’re using a CDP, you can save headaches and extra labor by using a tool that has compliance and privacy baked in, so to speak.
“What we’ve done is embrace some of this complexity and absorb it into the environment, so the marketer never even sees it,” said Corugedo. “What we do is with every implementation, we will implement a PII vault that keeps PII data super secure, and we can anonymize the marketing database.”
This way, personal information of individual customers (PII) is never violated.
“Marketers ultimately don’t necessarily need to have visibility to PII,” Corugedo explained “They like to see it for testing purposes and making sure that it looks right and everything, but the truth is we can do that in other ways without revealing PII.”
Having a handle on data quality adds to the confidence marketing teams have in creating segments and executing campaigns, and it can also help protect the customer’s privacy and guard against regulatory infringements.
Chris Wood draws on over 15 years of reporting experience as a B2B editor and journalist. At DMN, he served as associate editor, offering original analysis on the evolving marketing tech landscape. He has interviewed leaders in tech and policy, from Canva CEO Melanie Perkins, to former Cisco CEO John Chambers, and Vivek Kundra, appointed by Barack Obama as the country’s first federal CIO. He is especially interested in how new technologies, including voice and blockchain, are disrupting the marketing world as we know it. In 2019, he moderated a panel on “innovation theater” at Fintech Inn, in Vilnius. In addition to his marketing-focused reporting in industry trades like Robotics Trends, Modern Brewery Age and AdNation News, Wood has also written for KIRKUS, and contributes fiction, criticism and poetry to several leading book blogs. He studied English at Fairfield University, and was born in Springfield, Massachusetts. He lives in New York.