Successfully managing your streaming data infrastructure is a tough nut to crack for data teams around the globe.
Data generated in real-time from multiple sources requires swift real-time data processing and analysis to derive insights. The volume, velocity, and complexity of streaming data make it hard to manage and make sense of it.
For instance, imagine you are part of a data science team responsible for analyzing data from sensors monitoring machine performance on a manufacturing floor.
The data is constantly streaming in real-time, and you need to process and analyze it quickly to detect any issues affecting machine performance. However, the sheer amount of data coming in can make it challenging to process it all on time and identify patterns or anomalies that indicate a problem.
This is where DataOps comes in, offering a streamlined, automated, and collaborative approach to managing streaming data. DataOps can help data teams like the one we just described tackle the complexity of streaming data.
In this article, we will briefly discuss streaming data and its challenges. We’ll then explore the basics of DataOps for streaming data, its importance in modern data-driven organizations, and how it tackles the common challenges of streaming data.
Streaming Data and its Challenges
First thing’s first: what is streaming data?
Streaming data is a continuous flow of data that is generated from various sources such as sensors, social media, transactional systems, and other IoT devices. This type of data is characterized by its high velocity, high volume, and high variety.
Streaming data poses a significant challenge for organizations as they need to process this data in real-time and extract insights to make informed decisions. This requires a different approach to data management and analysis that can keep up with the pace of the data flow.
Here are a few of the common challenges of streaming data.
1. Streaming data creates data engineering challenges
Firstly, traditional data processing techniques that work well with static or batch data may not be suitable for streaming data. This is because streaming data is generated continuously in real time and requires real-time processing and analysis, which is different from processing static or batch data.
Streaming data infrastructure is event-based — it must transmit a change event each time the source data changes. This requires more sophisticated engineering than traditional batch processing, which polls the data for changes at regular intervals.
Also unlike traditional batch data that is typically predictable, streaming data is often unpredictable in terms of volume, where it can vary widely and change rapidly, which can make it difficult to plan for and manage.
2. Streaming data comes in large quantities.
As mentioned earlier, streaming data usually comes in high volume and high velocity. This means you must be able to process and analyze large amounts of data quickly and accurately.
This can be challenging: you need specialized tools and techniques to process streaming data in real time and handle challenges like:
- Varying data frequency.
- Data arriving out of order.
- Missing or incomplete data points.
3. Streaming data requires continuous monitoring
Another challenge with streaming data is that it requires continuous monitoring and adjustment. Unlike static or batch data, streaming data is constantly changing, which impacts all your downstream applications.
Data scientists, for example, must be able to continuously monitor data quality, update models and algorithms, and adjust their analysis and processing techniques as needed.
4. It’s hard to collaborate on streaming data.
The complexity of streaming data can make it difficult to collaborate across data teams. Different teams may use different tools, techniques, and languages to process streaming data, which can make it difficult to share code and collaborate on projects. This can lead to inconsistent data management and analysis, which can negatively impact the accuracy and reliability of insights generated from streaming data.
Overall, streaming data presents unique challenges for data teams that require specialized skills, tools, and techniques to effectively manage and analyze. DataOps can help by providing a framework of best practices for managing streaming data.
What is DataOps?
Many data teams fail because they focus on people and tools and ignore processes.
It’s similar to playing a sport with people and equipment but no game plan describing how everyone and everything should work together — failure would be inevitable.
DataOps defines the processes that keep everything in smooth working order.
DataOps is a methodology that aims to improve the efficiency and effectiveness of data-related operations by integrating people, processes, and technology. It borrows principles from DevOps and Agile methodologies to create a collaborative and agile environment that can handle the complexities of modern data management, especially the ability to minimize the lifecycle time for new data analytics and solutions.
DataOps basically combines data tools and data talents by providing a set of guidelines to improve rapid response data analytics, and the efficiency and effectiveness of data-related operations in an organization.
DataOps for Streaming Data
In the context of streaming data, DataOps provides a framework for managing the data ingestion, processing, analysis, and delivery pipeline of the data in real time. By emphasizing collaboration, automation, and continuous integration and delivery, DataOps enables organizations to manage their data operations more efficiently and effectively.
DataOps brings together data engineers, data analysts, data scientists, and business stakeholders to work together on data-related projects. This collaboration helps to create a culture of data-driven decision-making, which is critical in today’s data-driven organizations. With DataOps, data teams can manage streaming data more effectively, and generate valuable insights in real-time to drive business success.
DataOps can help solve the challenges associated with streaming data in several ways:
DataOps emphasizes automation, which can help data scientists to process and analyze streaming data more efficiently. By using open-source frameworks like Apache NiFi and Apache Kafka — or a DataOps platform like Estuary Flow — your data team can automate the collection, validation, transformation, and analysis of streaming data.
This reduces the need for manual intervention and frees up time to focus on higher-level tasks like model development, data analysis, and data visualization.
Continuous Integration and Delivery (CI/CD) Principles
Streaming data requires real-time processing, analysis, and delivery. Therefore, it is important to adopt a continuous integration and delivery (CI/CD) approach to ensure that changes to the data pipeline are tested and deployed seamlessly. This includes using automation tools for building, testing, and deploying data pipelines and adopting version control for managing changes.
By using CI/CD, you can quickly make changes to data pipelines in response to changes in the data, which can help improve the accuracy and reliability of insights generated from streaming data.
DataOps encourages collaboration between data science teams and other stakeholders involved in data operations because it is crucial for success in streaming data management.
By fostering a culture of collaboration, and encouraging cross-functional teams to work together to design, implement, and optimize data pipelines. This includes using collaborative tools like JIRA, Slack, and Confluence for managing workflows, sharing knowledge, and tracking progress.
Also, this DataOps collaborative framework helps your team:
- Collaborate on code.
- Share best practices.
- Work together to identify and resolve data quality issues.
Streaming data can be noisy with data arriving in different formats, at different frequencies, and with varying degrees of accuracy. As a result, it is critical to ensure that streaming data is of high quality.
DataOps incorporates quality control measures like data validation checks, data profiling, and data cleansing to ensure that the data used in data analysis is accurate and consistent. This helps you avoid errors and inconsistencies in data analysis, which can improve the reliability and accuracy of insights generated from streaming data.
The preferred mode for getting insight from streaming data is in real-time. Therefore, it is important to use real-time analytics tools and techniques to extract actionable insights from streaming data.
You can perform basic transformations on the fly as part of your data streaming pipeline. For in-depth analytics, it’s best to connect your streaming pipeline to an analytics hub like a real-time data warehouse.
Part of DataOps for streaming data is ensuring that monitoring is integrated with the data pipeline, because streaming data can be volatile, with sudden spikes or dips in data volume or velocity.
Therefore, it’s important to monitor streaming data in real time and set up alerts for anomalies or errors. This includes using monitoring tools for tracking data quality, data latency, and system performance and setting up alerts for critical events. Such tools include Prometheus and Grafana. If you use a UI-based data streaming data platform, monitoring may also be built in.
In conclusion, DataOps is a methodology that provides a streamlined and automated approach to managing streaming data.
By emphasizing data quality, adopting continuous integration and delivery, using real-time analytics, monitoring, and alerting, and fostering collaboration, your organization can unlock the power of streaming data and gain valuable insights in real time.
DataOps can create an agile and collaborative environment that can handle the complexities of streaming data management processes, which can help them to generate more meaningful insights and drive better business outcomes.
How do the principles of DataOps show up in your team’s real-time data strategy? Let us know in the comments or join the Estuary community discussion on Slack.