IT automation: Delivering big data’s benefits

As accessible means of delivering the benefits of big data, job scheduling and workload automation solve the challenge of keeping up with data warehousing practices.

Written by Colin Beasty. Last Updated:
Optimize the value of Informatica PowerCenter with workload automation and orchestration

In recent years, the democratization of analytics, reporting and business intelligence (BI) solutions have both improved and complicated data integration and data warehousing models. Add the growing complexity and volume of information thanks to big data, and it’s no surprise that integrating data from multiple sources to drive ETL and data warehousing processes is becoming increasingly hard to achieve.

Your IT organization may be between a rock and a hard place. On one hand, you have your business users — the consumers of data — and the pressure to seamlessly and efficiently update data warehousing processes to better meet business demands. 

On the other hand, the complexity of these underlying data warehousing processes is largely driven by the increasing numbers of data integration solutions, tools and data sources.

The evolution of data warehousing

The single data warehouse model is dead; a heterogeneous collection of data warehouses is the new model and is forcing IT organizations to streamline the movement of data between multiple repositories in support of real-time analytics.

IT organizations may encounter serious limitations in attempts to automate. For example, nearly all data warehouse, ETL and BI solutions have native batch scheduling capabilities, but they’re limited in their respective functionality for scheduling on their respective systems. Your team may be forced to rely on error-prone and time-consuming scripting to pass data and manage dependencies between the various components of the modern data warehousing process.

Unfortunately, scripting builds a barrier to garnering the benefits from a concept like “agile BI.” Relying on scripting to manage the extracting, warehousing and reporting processes makes it impossible to prioritize business requirements, which increasingly require the ability to run reports on-demand or on an inter-day cycle.

The goal behind IT automation should be to take an “architectural” approach to unifying point solutions into a single framework. Done properly, this will give you ease of authorship, control and upkeep and eliminate common sources of errors. When you integrate all data pathways with automated, repeatable processes, you get visibility over all steps of your data warehousing process:

These automations have the power to boost your returns on big data by allowing IT to reduce latency and increase data quality.

Today’s data warehousing challenges

As organizations embrace multi-cloud strategies and hybrid data environments, it becomes more complex to manage data across different platforms. This complexity makes it even more important to pay close attention to data consistency, security and performance. Modern data environments often involve various data types and require sophisticated automation to aggregate and integrate essential data.

Evolving regulations

Data governance and compliance are now leading concerns, especially for those impacted by strict regulations such as GDPR and CCPA. Organizations must also ensure their data governance practices align with global data privacy laws.

Automation can be critical in maintaining data lineage, auditing and access controls to reduce the risk of non-compliance. Automated workflows can handle these tasks more efficiently and accurately than manual processes, so all your data activities can be logged and traceable.

Demand for predictive power

Integrating artificial intelligence (AI) and machine learning (ML) into data warehousing is another significant challenge. Many organizations want to leverage these technologies for predictive analytics, but incorporating them into existing data warehousing processes can be difficult. 

Automated workflows that include AI/ML model training, deployment and monitoring can streamline these integrations and make your data analytics more accessible and scalable. Your teams can also explore new models rather than spending time managing data pipelines.

Need to manage data lakes

A growing reliance on data lakes for storing vast amounts of unstructured data adds another layer of intricacy to big data management. There are a lot of steps that need to be executed properly to make big data usable, and some businesses are struggling to catch up to these demands.

Automation tools can take care of data ingestion, storage and retrieval so you can achieve scalability without being bogged down in manual data management tasks.

Embracing modern automation tools

Modern automation tools can address the above challenges in a number of ways.

Tools that enable data analysts and data scientists to efficiently collect data, process it and derive insights can prime your business to stay compliant while scaling. Many of these tools can enable smooth data flow by bringing together disparate data sources by leveraging APIs.

Automation tools are also capable of enhancing data preparation and data transformation. Rather than managing repetitive tasks to ensure data is clean and consistent, data teams can put their efforts towards analysis that supports stakeholders in making better decisions.

As top automation tools are scalable, they can support efficient management of data lakes as your data volumes grow. Less worry about handling unstructured data means more focus on business goals.

The automation process can help you maintain data quality with consistent data validation procedures. Therefore, automation tools are often cost-effective solutions for monitoring metrics and meeting KPIs.

Find out how easy it is to address your current and future challenges using automation in big data applications. Demo ActiveBatch to explore the benefits of easy-to-implement workload automation.

FAQs about automation in big data

What is an example of data automation?

Data automation involves the use of technology to perform data-related tasks with minimal human intervention. An example of data automation is using extract, transform and load (ETL) tools to manage data flows between different systems.

For instance, in a retail company, data from various sources such as sales transactions, inventory systems and customer feedback can automatically be extracted, transformed into a consistent format and loaded into a data warehouse. The data will then be up to date, accurate and readily available for analysis without manual effort. Tools like Apache NiFi, Talend or Informatica can be used to automate these ETL processes. Integrating these tools with various apps can ensure seamless data flow and real-time analytics.

Discover how to simplify big data with data orchestration

How to automate big data testing?

Automating big data testing involves several steps to ensure data pipelines are accurate and reliable. Here are the key steps:

1. Define test cases and data requirements. Identify the key scenarios that need to be tested, including data ingestion, processing and output validation. Specify the data sets and criteria for correctness.
2. Generate and mock. Use tools to generate large volumes of test data or mock data sets that represent real-world scenarios. You could use a synthetic data generator.
3. Automate test execution. Use test automation frameworks such as Apache Spark’s testing utilities, JUnit or TestNG integrated with CI/CD pipelines. This way, your tests will run automatically on code changes.
4. Validate and verify. Implement automated validation scripts to compare expected and actual results. This might include schema validation, data integrity check and performance benchmarking. Custom Python scripts or validation tools can help.
5. Monitor and report continuously. Set up monitoring to track the performance and accuracy of your data processing jobs. Use automated reporting tools to alert your teams to any issues immediately.

These steps ensure every aspect of your data pipeline it thoroughly tested, giving you confidence it the accuracy and reliability of your big data analytics.

Learn more about the ETL automation process and testing

What is automation in analytics?

Automation in analytics refers to the use of software and algorithms to perform data analysis tasks without manual intervention. It encompasses a range of activities, including data collection and preparation, data analysis and data visualization. With the help of machine learning algorithms and statistical models, plus auto-updated dashboards, you can use data insights to trigger actions and reduce the burden on your team. Ultimately, data automation tools help you make data-driven decisions.

Optimize and simplify your data center management with ActiveBatch Workload Automation

What is the objective of data automation?

The objective of data automation is to streamline and optimize data-related tasks to increase efficiency, accuracy and speed. Because automated systems can handle large volumes of data and complex processes more effectively than humans, they support scaling while maintaining consistency. Data management, analysis and utilization are simpler with automation.

Learn how to free your data with IT automation.