What is DataOps?DataOps team rolesData specialistsData engineersPrincipal data engineersHow is DataOps different from DevOps?Why invest in DataOps?The importance of telemetry data to network visibilityHow DataOps operationalizes network telemetry dataLeverage telemetry data with Kentik to solve network issues before they start
Network telemetry and DataOps play a critical role in enhancing network visibility. By combining both, organizations can improve network visibility and gain insight to help them optimize their network performance, improve security, and enhance the overall user experience.
Network telemetry and DataOps are two concepts that play a critical role in enhancing network visibility. With modern networks’ increasing complexity and scale, it has become essential to collect and analyze data from various sources to gain insights into network performance, security, and availability. Network telemetry is the process of collecting and analyzing data from network devices, including switches, routers, firewalls, and servers, to gain visibility into network traffic and performance.
By combining network telemetry and DataOps, organizations can improve their network visibility and gain actionable insights that can help them optimize their network performance, improve security, and enhance the overall user experience.
What is DataOps?
DataOps is a methodology that aims to streamline and automate managing and delivering data throughout its lifecycle, from ingestion to analysis and visualization. It is an extension of DevOps principles and practices to data management, enabling organizations to manage and automate data pipelines for quality, accuracy, and reliability.
The DataOps ecosystem comprises several components, including people, processes, and tools. At the heart of DataOps is the agile development methodology, which emphasizes collaboration, iteration, and continuous delivery. Data scientists play a critical role in the DataOps ecosystem, leveraging advanced analytics and machine learning techniques to gain insights from large and complex data sets.
DataOps also involves a range of tools and technologies, including data integration and ETL (extract, transform, load) tools, data quality and governance tools, data catalog and metadata management tools, and data visualization and reporting tools. DataOps strategies require a robust data infrastructure, including data warehouses, data lakes, caches, and other data storage and processing systems.
DataOps team roles
In a DataOps team, several key roles work together to ensure the data pipeline is efficient, reliable, and scalable. These roles include data specialists, data engineers, and principal data engineers.
Data specialists are responsible for ensuring the quality of data and its suitability for analysis. They work closely with data owners and data consumers to understand their needs and requirements, and they use their expertise to ensure that data is collected, processed, and stored correctly. Data specialists also ensure that data is accessible to those who need it, and they monitor the data pipeline to identify and resolve any issues.
Data engineers are responsible for building and maintaining the data pipeline infrastructure. They work with data scientists and specialists to design and implement data processing workflows, ensuring that data is transformed and loaded into the appropriate data stores. Data engineers also ensure that the data pipeline is scalable, reliable, and efficient, and they monitor the pipeline to identify and address any bottlenecks or issues.
Principal data engineers
Principal data engineers are senior members of the DataOps team who oversee the design and development of the data pipeline infrastructure. They work closely with data engineers to ensure the pipeline is robust and scalable. They also work with data scientists and specialists to ensure that the pipeline meets the needs of the business. Principal data engineers also play a crucial role in identifying and evaluating new technologies and tools that can improve the efficiency and effectiveness of the data pipeline.
How is DataOps different from DevOps?
While both DevOps and DataOps efforts can be applied to network observability, they have different approaches and focus on other aspects of network management.
DevOps focuses on the software development lifecycle and is principally a telemetry source and operational context for network operators. DataOps specifically targets data management and delivery, leveraging advanced analytics and machine learning techniques to gain insights and improve network performance.
For NetOps, DataOps represents a pivotal approach to successfully managing and leveraging network data in highly scaled systems.
Why invest in DataOps?
By using DataOps, businesses can ensure that the data they collect and analyze is high-quality, accurate, and reliable, which is essential for effective data analytics and analysis. With DataOps, businesses can improve their data agility and accelerate their time to insights, enabling them to make faster and better-informed decisions. These improvements can lead to operational efficiency, reduced costs, and improved customer satisfaction, all critical for meeting the demands of today’s business environment.
The importance of telemetry data to network visibility
Telemetry data is essential for keeping a network up and running. It provides real-time visibility into the performance of network devices, applications, and traffic, enabling network operators to detect and resolve issues quickly. Telemetry data includes information such as network traffic patterns, packet loss, latency, and jitter, as well as device metrics such as CPU utilization, memory usage, and interface errors.
With network telemetry data, network operators can gain a holistic view of the network and identify performance issues before they impact end users. For example, suppose telemetry data shows network traffic congested at a particular interface. In that case, network operators can take proactive measures to alleviate the congestion, such as increasing bandwidth or optimizing traffic routing.
Telemetry data is also critical for network security. Network operators can detect and respond to security threats in real-time, such as DDoS attacks or unauthorized access attempts, by monitoring telemetry data.
To ensure the effectiveness of telemetry data, it is essential to enrich it with context and metadata, such as device and application information. This enables network operators to better understand the performance of the network and the root causes of issues. By following best practices for enriching telemetry data, network operators can improve network observability and ensure the reliability and availability of their networks.
How DataOps operationalizes network telemetry data
DataOps leverages network telemetry to remove bottlenecks by providing end-to-end visibility and actionable intelligence. DataOps can identify issues and prioritize responses and optimizations based on key performance indicators (KPIs), like connections per second, latency, and packet loss, by aggregating and analyzing real-time network performance telemetry.
This data can be used to automate workflows and improve data governance, ensuring that data is accurate, reliable, and compliant. With network telemetry, DataOps teams can gain deeper insights into network performance and make more informed decisions, ultimately leading to improved network visibility and optimized performance.
Leverage telemetry data with Kentik to solve network issues before they start
Kentik can help companies solve network issues before they start by leveraging telemetry data to provide real-time network observability and analytics. Kentik ingests telemetry data from a wide range of sources, including network flows, SNMP, BGP, and more, providing end-to-end visibility across hybrid and multi-cloud environments.
Kentik’s machine learning algorithms and advanced analytics provide actionable insights into network performance, security, and capacity planning, enabling companies to identify and resolve issues before they impact end-users proactively. With Kentik, companies can set up custom alerts and thresholds to monitor network KPIs and receive automated notifications when problems arise.
In addition to its advanced analytics and automation capabilities, Kentik provides robust data governance features, enabling companies to ensure that their data is accurate, reliable, and compliant. Kentik’s user-friendly dashboards and reporting tools enable companies to quickly and easily visualize their network data and gain insights into network performance.
Kentik provides companies with the tools and insights they need to optimize network performance, proactively identify and resolve issues, and ensure the reliability and availability of their networks.
To get started with Kentik, sign up for a demo today.