How data lakehouses and observability can promote DevOps maturity

DevOps teams face the challenge of accurately and properly harnessing the potential of cloud-based analytics in the face of ever-increasing volumes of data. The evolution of DevOps has shown that embracing modern architectures and tools is essential to manage this growing complexity effectively. Not only is the traditional approach of implementing data warehouses and data lakes becoming insufficient, but the massive spike in data has also affected the cost of using both solutions.

A data lake is a large storage repository for any kind of data, regardless of format (structured, semi-structured, unstructured). A data warehouse stores data and is designed for efficient querying and reporting on predefined questions. The problem with data lakes is that they can become messy and unreliable if there is no proper quality control, while with data warehouses, it is always a struggle to adapt to new data types or business needs. This is where data lakehouses come in.

Coupled with observability, a data lakehouse can address these challenges and promote DevOps maturity. This is because a data lakehouse provides flexible storage for unstructured data, as seen in a data lake, and the data management functions of a data warehouse. Adding in observability transforms a data lakehouse into a modern data platform for managing the volumes of structured and unstructured data organizations generate today.

What is DevOps maturity?

DevOps maturity refers to the level of proficiency and effectiveness of a DevOps team when managing and optimizing their systems and applications to achieve the desired results.

The DevOps maturity model outlines stages that represent increasing levels of DevOps effectiveness. These are centered around the ability to:

• Manage complex IT environments
• Collaborate effectively
• Drive informed decision-making
• Continuously improve performance and innovation
• Continuously deliver value to customers in terms of DevOps culture

Organizations vary in maturity based on their size, goals, and industry. Their maturity is evaluated for each specific area, such as infrastructure and ease of collaboration, with a cumulative score.

Achieving DevOps maturity can be difficult, especially when handling massive amounts of data. Observability and data lakehouses stand out as essential catalysts for advancing DevOps maturity, providing extensive insights and resilient capabilities in data management.

Observability and its role in DevOps

Observability provides teams with the means to better understand system performance and behaviors. It encompasses the tools and methodologies that aid DevOps teams in monitoring, logging, collecting, correlating, and analyzing massive amounts of data.

Observability empowers DevOps teams to achieve maturity by helping them shift from reactive firefighting to proactive prevention through the insights it provides. By implementing observability, teams can quickly identify and resolve issues, reduce mean time to recovery (MTTR), and improve overall system reliability and stability. Additionally, observability fosters a collaborative and data-driven environment that propels organizations toward faster deployments and higher-quality software.

All of this, in turn, promotes DevOps maturity.

Observability vs. monitoring

Observability focuses on understanding the "why" behind system behavior while monitoring concentrates on the "what."

For troubleshooting DevOps issues, you need to understand why your application entered a specific state, identify the responsible component, and strategize on how to avoid it. Observability helps us answer questions such as:

• What is the current state of the application and infrastructure?
• Are there any errors or performance issues?
• What is the underlying cause of identified issues?
• How did specific code changes or configuration updates impact the system's behavior?
• How are different components of the system interacting?
• How can we improve overall system performance?

Observability is increasingly important for ensuring the reliability and performance of software and cloud environments. It is integrated into the DevOps workflow to promote continuous improvement of the entire system.

Observability will help:

• Detect issues in real time
• Continuously monitor system performance and optimize accordingly
• Identify future resources required by analyzing the observability report
• Deliver flexibility to understand unpredictable events

Data lakehouses and DevOps maturity

The continual growth of data volumes complicates orchestration, processing, and analysis, making it more challenging for DevOps teams to transform data into actionable insights.

Managing this data sprawl requires a more adaptable and flexible system than traditional data warehouses. When using a data silo, organizations face problems such as:

• Limited scalability for growing data volumes
• Difficulty integrating data across silos
• Lack of flexibility for new data types
• Resource inefficiency due to data duplication

A data lakehouse solves these problems.

Fig. 1: Data architecture evolution to a data lakehouse. (Source: Unstructured Data Quick Tips)

A data lakehouse is a modern hybrid approach to data management. Like a data lake, it can store unstructured data, enabling the system to be more flexible and scalable. At the same time, it provides a layer for organizing, structuring, and processing the data, similar to the schema-on-read approach of a data warehouse; this enables business intelligence and high-performance machine learning workflows.

DevOps teams can also store data from different sources in a data lakehouse. Below are some ways data lakehouses promote DevOps maturity for developers/IT teams.

Parallel processing

Unlike data warehouses, which typically process queries on a single server, data lakehouses leverage massive parallel processing (MPP) for distributed query execution. MPP distributes queries across a cluster of nodes or servers, enabling them to work on different data segments simultaneously. This significantly reduces query response latency, which is especially beneficial for complex queries involving massive data sets.

Data lakes can also store vast amounts of data. However, they often lack the structure and processing power to achieve the same level of parallelism and query efficiency as a data lakehouse

Streamlined data management

Data lakehouses eliminate the need for data silos and fragmentation. They unify structured, semi-structured, and unstructured data, enabling centralized management and analysis. This translates into faster insights, improved resource allocation, and easier decision-making.

High-cardinality data mastery

Data lakehouses excel at ingesting and analyzing high-cardinality data, a data column containing numerous unique values. This is crucial for understanding user behavior, optimizing marketing campaigns, and making data-driven predictions.

Unlike traditional data warehouses, which are often optimized for structured data with a limited number of unique values per attribute, data lakehouses can efficiently handle data with high cardinality. While data lakes can also store high-cardinality data, they often lack the structure and processing power to analyze it effectively.

Data lakehouses combine a data lake’s flexibility with a data warehouse’s querying features, allowing you to process high-cardinality data and gain hidden insights.

Continuous performance optimization

Data lakehouses enable continuous analysis of the data lakehouse itself and other integrated services, identifying potential bottlenecks and optimizing resource utilization. This proactive approach helps prevent performance issues before they impact data processing or user queries.

By analyzing usage patterns and resource allocation, data lakehouses ensure smooth and efficient operations at scale.

Enhanced compliance

Data lakehouses provide a secure environment for storing and managing all of your data. This centralized platform simplifies data access control and promotes compliance with regulatory standards including GDPR and HIPAA.

Driving DevOps maturity

DevOps maturity is all about streamlining an organization's software development lifecycle. By using observability and data lakehouses together, a DevOps team can become more mature in the following ways:

• Faster problem-solving: This also reduces downtime and improves software quality.
• Data-driven decisions: Nuanced insights help DevOps teams make informed decisions.
• Improved collaboration: Dev, ops, and data teams work together, sharing insights and utilizing a common data repository; this fosters problem-solving and innovation.
• Enhanced predictive analytics: Data lakehouses support extended data retention; combined with real-time observability metrics, DevOps teams can anticipate potential issues and optimize resource allocation.

Use case: Observability and data lakehouses in a DevOps workflow

Organization: Checkout.com

Goal: Preventing, detecting, and resolving data freshness issues in a fintech company

Challenge: Managing data freshness at scale

Checkout.com manages a massive data infrastructure, with over 300 active data platform users daily. They monitor more than 4,600 data sets and handle over 1,200 dbt models that perform data transformations every single day.

The amount of data being generated led the company to make observability “a crucial part of (their) day-to-day business,” adopting “data observability—or essentially data quality and data freshness—quite early on in (their) journey,” according to Senior Data Engineer Martynas Matimaitis.

Timely delivery of data is crucial, as outdated data can impede the progress of data-driven organizations. Although setting up data freshness alerts is not inherently complex, managing them at scale poses significant challenges.

Solution: Automated observability with a data lakehouse

By leveraging a data lakehouse for flexible data storage and observability tools for automated monitoring, Checkout.com addressed the challenges surrounding data freshness:

1. Centralized data storage: The data lakehouse provided a unified platform for ingesting and storing all data, regardless of format; this simplified data lineage tracking and freshness monitoring.
2. Automated monitoring: Observability tools continuously monitored data pipelines to identify potential delays and data staleness; automated alerts ensured issues were addressed promptly.
3. Scalable management: By automating data freshness monitoring, Checkout.com reduced manual workloads and ensured consistent data quality across their massive data infrastructure.

Factors to consider

Careful consideration is essential before introducing observability and data lakehouses into your DevOps workflow:

• Start small by focusing on critical systems and gradually incorporate other areas as expertise and resources allow.
• Keep specific organizational needs and budget constraints in mind when selecting a tool.
• Plan carefully to ensure seamless integration of the data lakehouse with existing systems for a smooth data flow and easy access.
• Clearly define a comprehensive data governance strategy that includes policies for data ownership, access control, retention, and deletion.
• Train your team to effectively utilize and interpret observability data.
• Establish security measures to ensure sensitive data within the data lakehouse is safe.

Conclusion

The architecture of a data lakehouse coupled with observability empowers DevOps teams to extract actionable insights from their observability, security, and business data. This approach allows organizations to store massive volumes of data cost-efficiently while achieving a holistic view of their systems and applications.

In this way, DevOps teams can proactively identify and address potential issues, streamline monitoring and analysis processes, and ultimately foster a culture of continuous improvement and innovation.