From generating two zettabytes of data in 2010, we now generate 120 zettabytes annually. Statista predicts that by 2025, we will generate over 180 zettabytes of data globally.
No wonder, over 78% of data chiefs plan to increase their data investments this year.
Given that companies receive data from more than 400 sources, the sheer volume and diversity of this raw data can be overwhelming. Companies must standardize and structure the data to make it intelligible and usable, which is where data pipelines come to the rescue.
A data pipeline involves a series of steps, such as ingesting data from various sources, processing, cleaning, transforming, and storing it for future use. Companies can later use technologies like Machine Learning (ML) to generate insights and publish dashboards and reports to present to stakeholders for decision-making.
Throughout the process, data is transferred between different systems and applications. This poses a challenge for data scientists, who have to clean and transform the data and make it usable. Additionally, they have to debug and upgrade data pipelines and check integration between various data pipelines. The problem worsens when the data volume increases, calling for automating the pipelines.
Why Should Companies Automate Data Pipelines?
Data pipeline automation helps data scientists extract data from various sources and cleanse, organize, and prepare the data for use. It eliminates the manual work involved in data extraction and transformation and provides usable data for stakeholders to make quick decisions.
Here are a few benefits of automating data pipelines:
- It automates repetitive tasks like transferring and processing data, which gives data scientists more time to do strategic work. Also, since these tasks are automated, there are very few errors or inconsistencies in the data. The high-quality data provides better insights and enables the decision-makers to make informed decisions faster.
- Automated pipelines also reduce manual labor costs and the costs associated with errors. This helps the company save a substantial amount of money.
- Automated pipelines can easily manage fluctuating data volumes and scale up or down the resources accordingly.
While data pipeline automation takes away the major burden of standardizing data from data scientists, there are some hurdles that data scientists will have to overcome in the process.
- The data pipelines should be well-equipped to manage large data volumes at high velocity. Processing them in real time can be challenging without a reliable and scalable infrastructure.
- If not addressed early, errors in incoming data can percolate down the pipeline, leading to inaccuracies and inconsistencies in the data.
- Data latency can be a problem when companies work on mission-critical projects. Data scientists must ensure low latency during the automation process, especially when they receive data from disparate sources or in complex and unstandardized formats.
- Since data scientists integrate data from different databases, APIs, and devices, centralizing them can be difficult. The data silos have to be eliminated before the automation begins.
- Ensuring data reliability and consistency becomes a problem when it is stored in distributed and real-time environments. The pipelines must be fault-tolerant and capable of managing system failures, network interruptions, and data spikes.
- As the business scales, the pipeline must become expandable and scale horizontally to manage the growing workload.
Addressing these shortcomings will help data scientists automate the data pipelines more effectively and aid business decision-makers in making informed decisions on time.
How Can DataOps Improve Data Pipeline Automation?
Companies can use DataOps to improve the effectiveness of data pipeline automation.
Gartner defines DataOps as a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and consumers across an organization.
In other words, DataOps is a methodology that integrates DevOps teams, tools, and processes to automate data flows and ensure timely access to data for all stakeholders to make decisions. It provides a framework that promotes more collaboration among teams and leverages continuous integration and deployment principles of agile development to streamline data pipeline management.
Here’s how DataOps can add more value to data pipeline automation:
- Speed and delivery: By automating data flow, ingestion, transformation, processing, and deployment, DataOps ensures that reliable data is always available for stakeholders to use. It enables companies to make fast business decisions and respond to market changes before their competitors.
- Better data quality: DataOps enables data scientists to conduct quality checks at different pipeline stages to ensure the availability of accurate, consistent, and complete data for decision-making. The constant monitoring and observability of data pipelines also play a role in improving data quality. Data scientists can monitor the data flow, identify errors, and resolve them proactively.
- More communication between stakeholders: DataOps encourages collaboration among engineers, data scientists, business users, and other key decision-makers. It breaks the silos between the teams, promotes knowledge sharing, and ensures everybody benefits from the data. It improves the efficiency among teams.
- More control over the data pipeline: DataOps’ version control capabilities allow data scientists to have more control over the data pipeline. They can track changes in data pipelines and codes and roll them back in case of any problem. This improves data transparency and encourages teams to experiment more.
Conclusion
As data volume increases, companies will have difficulty making sense of it. Add to this the burden of extracting and transforming data into intelligible insights, which can become time-consuming and complex as the company scales.
Companies can address these issues by automating the data pipeline and using the DataOps methodology. They can bridge the data silos, improve collaboration among various stakeholders, automate repetitive tasks, and provide accurate and real-time data for timely decision-making. Both of them combined together can help companies improve the efficiency and reliability of data.
