Skill and experience command a premium in the world of data engineering—the value of a professional's time is paramount. This is especially true when considering the tools and methodologies employed in managing modern data workflows.
Traditionally, data teams have relied on open-source solutions for data orchestration. Doing so, however, requires extensive coding and a solid understanding of dependencies. Moreover, using open-source solutions for data orchestration makes it increasingly difficult to fully leverage capabilities offered by cloud-based platforms like Amazon Web Services (AWS), Google Cloud, and Azure.
All the above begs a crucial question—at what point does the time, energy, and talent required by open-source data orchestration become counterproductive (especially when data engineers rank among the highest-paid professionals in any given organization)?
A second, equally pressing question: Isn't it time for tradition to make way for the growing benefits of automated data orchestration?
With all due respect to tradition, let’s do some rabble-rousing, shall we?
What is data orchestration?
Regardless of whether the process itself is manual or automated, data orchestration involves the systematic management of data flow across various sources. These data sources can be diverse, including data lakes and cloud-based systems, external APIs, and on-premise databases.
Upon ingestion, orchestration platforms standardize, cleanse, and consolidate data for analytical processing. Considering the ever-increasing volume that constitutes Big Data and the variety and velocity with which it’s now generated, data orchestration is a cornerstone in many data-driven organizations.
In these organizations, the data orchestration process consists of several critical steps. Each step plays a vital role in managing the lifecycle of data, from producers to consumers.
- Data collection and aggregation: As noted, orchestration begins with data integration. Connectors—specialized software components or modules—enable orchestration solutions to ingest data as needed.
- Data preparation: Once ingested, data is then prepared to enter an organization’s data lifecycle in a usable state. Substeps at this point will also set the stage for high data quality—checking data completeness, standardization, integrity, and whether any deduplication is required. The preparation step also ensures a given orchestration platform or tool can understand and work with the data moving forward.
- Data transformation: The orchestration solution works to make sure the data will serve any downstream objectives required of it. Customer data, as an example, for use in data analytics or data analysis may need very different permutations than if the same data were to power a business intelligence (BI) dashboard. Data transformation finishes what data preparation begins—further standardizing data formats, converting data types, and consolidating everything into one coherent dataset.
- Data synchronization (i.e., data sync): The orchestration solution will then systematically reconcile data differences and resolve conflicts that arise due to the disparate nature of data sources and the varied timing of data updates. This process ensures that all systems reflect the most current, cleansed, and transformed version of the data.
- Data delivery to consumers: Finally, having prepared, transformed, and synchronized the data, it can be delivered directly to data consumers or to a repository, like a data lake or data warehouse.
Why manual data orchestration is getting costly
Manual data orchestration, while streamlining the data management process, presents several challenges that can be expensive for organizations. Many of these expenses manifest in terms of resource allocation and operational efficiency. But the average data environment is growing more complicated by the day. This means the challenges of manual orchestration can quickly grow into business risks due to common factors:
Time-consuming execution
As data continues to grow in volume and variety, manual processing becomes increasingly time-consuming, leading to delays in insights and decision-making. There is also the intensified risk of missing critical market trends or opportunities due to slow data processing and analysis.
Error-prone operations
The rapid influx of data, coupled with its complexity, significantly raises the risk of manual errors. These mistakes can propagate through the data pipeline, leading to flawed analytics and potentially costly business decisions based on inaccurate data.
Limited scalability
Manual data handling is not equipped to adapt to sudden spikes in data volume, common in today's data-driven scenarios. This inability to scale swiftly can lead to bottlenecks and data processing backlogs, impacting timely decision-making and strategic planning.
Inconsistency in data handling
Manual processes often fail to uniformly handle data from varied sources such as IoT devices, social media, and transactional systems, leading to inconsistent data quality that can skew analytical outcomes and business strategies.
Resource intensiveness
Manual data management demands extensive manpower, which can be a significant financial burden for organizations. This heavy resource allocation to routine data tasks may divert attention and funding from more strategic initiatives, hindering overall business growth.
Inability to leverage advanced analytics
Manual processes often can't keep pace with the velocity of data, hindering real-time analysis and insights. Failure to keep pace with real-time data could result in outdated or irrelevant insights.
Difficulty in compliance and governance
The manual management of data across complex and evolving regulatory landscapes increases the risk of non-compliance. This challenge is amplified with the introduction of new data privacy laws and regulations, demanding more rigorous data handling procedures.
These complications highlight the need for automated and sophisticated data orchestration methods in handling the increasing complexity of modern data environments.
How automation makes orchestration work smarter
In comparison, automation of data orchestration processes can significantly help reduce labor costs and address many of the challenges mentioned above.
Reduced manual effort: Automating repetitive tasks like data extraction, transformation, and loading reduces the need for manual intervention. This not only saves time but also reduces the likelihood of errors that can occur with manual processes.
Improved data quality and consistency: Automation in data orchestration, particularly when leveraging tools like Apache Airflow or Prefect, streamlines processes such as ETL (extract, transform, load), enhancing data quality and consistency. Automated validation and cleansing processes ensure that the data is accurate and reliable.
Enhanced security and compliance: Automation in data orchestration can include security protocols and compliance checks as part of the workflow. This ensures that data is handled securely and in compliance with regulatory requirements, reducing the risk of breaches and non-compliance penalties.
Scalability and flexibility: Automated orchestration tools can scale up or down based on data volume and processing needs. This scalability ensures that data operations can grow with the business without requiring significant additional investment in infrastructure or labor.
Increased efficiency and faster insights: Automation speeds up the entire data orchestration process, from collection to analysis. This leads to faster time-to-insights, enabling businesses to make quicker decisions based on the most current data available.
Implementing automation in data orchestration requires a strategic approach. Data engineers must select tools that integrate well with existing systems, are scalable, and have robust security features. Additionally, they should establish monitoring and alerting mechanisms to quickly identify and resolve issues in the automated data workflows.
How to vet automated orchestration tools
When selecting tools for automating data orchestration, data engineers should consider the following criteria:
- Integration capabilities: Choose tools that can integrate with a wide range of data sources and destinations, including cloud-based services, on-premise databases, and various file formats.
- Scalability: The tool should be able to handle increasing volumes of data and complex data workflows without significant drops in performance.
- User-friendliness: Tools that offer a user-friendly interface and visual workflow design can be more easily adopted by teams, including those with less technical expertise.
- Security and compliance: Ensure that the tool provides robust security features, such as encryption, access controls, and auditing capabilities, to maintain data security and compliance with regulations.
- Monitoring and alerting: Tools should offer monitoring capabilities to track the health and performance of data pipelines and alerting mechanisms to quickly identify and address issues.
Tools to consider first for data orchestration automation
Open source
Prefect: A newer workflow management system that emphasizes ease of use and flexibility, Prefect features a Python-based approach to workflow automation.
Luigi: Developed (and still used by) Spotify, Luigi helps you build complex pipelines of batch jobs, handle dependency resolution, and create workflows.
Apache NiFi: A system to process and distribute data, NiFi supports data routing, transformation, and system mediation logic.
Non-open source
Talend: This platform offers various software and services for data integration, data management, enterprise application integration, data quality, cloud storage, and Big Data.
Shipyard: A cloud-based platform that provides tools for building, running, and orchestrating data workflows in the cloud, Shipyard makes it easier for teams to automate their data operations.
Matillion: This data transformation solution for cloud data warehouses enables users to transform and load data into various cloud data warehouse platforms.
Automation best practices to prioritize
There are many lists of best practices for automating data orchestration, but we feel the following deserve additional attention:
- Creating modular workflows: Endeavour to design workflows in a modular fashion, allowing for easier maintenance and scalability.
- Regular updates and maintenance: Keep all orchestration tools updated and conduct regular maintenance to ensure they are functioning optimally. Also, look for platforms and tools that can handle updates and maintenance on their own.
- Emphasize data quality: Make sure to implement checks and balances to ensure the quality of the data being processed. This includes validation, cleansing, and deduplication to prevent errors and inconsistencies in data flows.
- Monitoring and alerting: Set up robust monitoring systems for the data orchestration process. Ideally, this will include automated real-time alerts for any failures or bottlenecks, enabling quick responses to issues.
- Scalability considerations: It’s increasingly important to design data workflows with scalability in mind. This means considering the future growth in data volume and variety, ensuring the system can handle this growth without significant rework. Cloud-native platforms can offer significant advantages in this regard.
- Security measures, error handling, and recovery mechanisms: Ensure that data security is a priority. This includes securing data at rest and in transit, managing access controls, and adhering to compliance requirements. Additionally, implement robust error handling and recovery processes. This ensures that the system can handle and recover from failures with minimal data losses and downtime.
- Integration capabilities: Ensure that the data orchestration tools can integrate seamlessly with different data sources, databases, and all needed downstream applications.
Shipyard: The best way to sail the seas of automated data orchestration
The traditional approach of relying exclusively on open-source tools and extensive coding in data orchestration overlooks a crucial aspect of resource utilization. When considering the high compensation of data engineers and the plethora of tasks they manage, it becomes evident that optimizing their time is not just a matter of efficiency but also of strategic financial management.
Shipyard's platform, featuring over 150+ templates and blueprints that integrate seamlessly with artificial intelligence and machine learning models, offers a strategic solution to overcome bottlenecks in data orchestration.
By embracing such solutions, companies can redirect their valuable resources from repetitive coding tasks to more critical and impactful data challenges. Ready to start? Well, start for free with Shipyard. You’ll have your first workflow up and running in 10 minutes or less.
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