Automate Cloud Data Operations for BI and ML Data Pipelines

Businesses are in a rush to move their on-site data warehouse and Hadoop data analytics environments to the cloud. They are seeking solutions to facilitate and accelerate migration and ongoing operations. Migration, operationalization, development, and orchestration of data pipelines are now possible with the automation of cloud data operations. The apache-spark is one such platform that delivers high levels of automation that ease the burden on organizations that don’t have large data engineering teams, while also greatly accelerating time to value for new analytics use cases. ML certifications are gaining a lot of popularity; you can choose as per your suitability from the best certifications for machine learning online.


Blog contents

  • What is cloud automation?
  • Why is there a need for it?
  • Market Vendors
  • Conclusion


In this article, we talk about the automation of Cloud Data Operations for BI and ML Data Pipelines. Let’s get started!


What is cloud automation?

Cloud Automation is the software-based solution that automates cloud computing services installation, configuration, and management. In other words, cloud automation is all about leveraging technology to lower manual efforts in the cloud for repetitive tasks. Automating all Cloud environment modules is a complicated task. To the current difficulty, installing and deploying virtual machines, servers, storage, and virtual networks adds only. And, it’s not stopping here; after deploying the resources, you need to manage and monitor to ensure they are up to expectations.

Automation’s come a long way from being a must-have trend. A successful cloud journey is now one of the fundamentals, not only during migration but also for ongoing optimization. In this technology world, as the business pace increases, IT infrastructure keeps on scaling. This involves the demand for new services with quicker and constant data access. The IT team requires to move from manual to automated cloud resource management to meet those expectations. The cloud resources are too complicated for people to handle and manage in real-time for most organizations. The need for automation processes is becoming paramount as the cloud operations scale. Automation’s real significance is making cloud management tasks as efficient as possible to achieve the cloud’s promised value.


Why is there a need for it?

The need for better automation stems mainly from the immaturity of the pipelines in data analytics. The development of analytics and data pipelines, both simplistic and complex, is still a handcrafted process with minimal reuse and largely non-repeatable, managed by individuals working in isolation with different tools and approaches.

Even though new software tools are emerging to handle much of the repetitive data pipeline work that is generally handled by data engineers, Data science experts have tools like Kubeflow and Airflow to automate machine learning workflows. Still, data engineers need their DataOps tools for managing the pipeline.

As data and analytics pipelines become more complicated and development teams grow in size, organizations need to apply standard processes to govern data flow from one step of the data life cycle to the next – from transformation to analysis, data ingestion, and reporting. The goal should be to expand agility and cycle times while reducing data defects, providing developers and business users higher confidence in data analytics output.

Market Vendors

Many vendors are emerging in the automation of data pipeline work. Here is a list.

  • is a big player in the market. Its Autonomous Data Engine addresses a wide variety of data engineering tasks, from ingestion and data capture changes to data shaping and analytics preparation of data consumption.


  • Another DataOps outfit is DataKitchen, which automates the data pipeline. The company Cambridge, Massachusetts, uses a metaphor for cooking to describe its DataOps platform. Multiple people can share and follow the recipes while working in a data kitchen to create multi-ingredient dishes.


  • Nexla also provides a DataOps platform that automates the creation of data connections to databases and other repositories, the management of repetitive data transformation tasks, the management of data schemas, and the monitoring of data lineage in important data ecosystems like Hadoop. 


  • The Milbrae, California company that serves customers in e-commerce, insurance, travel, and healthcare, also helps automate data management in various data formats, like Parquet, ORC, and Avro.


  • Bedrock Data also supplies automation solutions for the data pipeline. Based in Boston, Massachusetts, the company sells a Fusion product that focuses on fusing data from applications like Salesforce and Marketo, such as Software as a Service (SaaS). The Software automatically creates and maintains a SQL data warehouse from the disparate data sources, allowing analysts to obtain it via BI visualization tools.


  • StreamSets also serve the emerging markets for the DataOps and automated data pipelines. The San Francisco, California company, is pushing its software offering as a “cross-platform data movement layer” that gives clients better visibility and control over their data pipelines’ performance, including data drift detection.


Most of these data pipeline products run on Hadoop and Spark environments and run on public clouds. They offer pre-built connectors to familiar data sources, such as HDFS, S3-compatible object stores, Excel, FTP, and relational and NoSQL databases.



With scalability and flexibility, the cloud is the playground to drive business growth. But this can only be accomplished when organizations tend to move towards a higher goal of achieving cloud-based IT efficiency, and automation is in perfect alignment with that goal. Many organizations lack the know-how and expertise to automate their cloud. So it’s recommended to have a platform for cloud management in place to assist you with automation. Cloud Automation is the only means to keep driving your organisation’s innovation engine by freeing up resources that need to spend time making strategic decisions. How strategically do you step up to automate your cloud depends on you. There are machine learning for beginners courses and training available online, do check them out today!