January 2022 Summaries

The cybersecurity talent shortage is a pressing issue, with nearly 600,000 unfilled positions in the US as of December 2021. To alleviate this shortage, organizations are adopting DevSecOps, which involves integrating security into the software development lifecycle (SDLC) and creating a shared responsibility model for security. This approach empowers developers to secure what they build at their pace, promoting better collaboration between DevOps and security professionals. By leveraging existing resources, such as log analytics tools, and investing in training and automation, organizations can transition from traditional DevOps to DevSecOps and build more secure applications. Secure software development best practices are also crucial, with a focus on enabling developers to use the tools they're comfortable with for security purposes. Ultimately, embracing a DevSecOps mindset allows organizations to take ownership of security, transforming the traditional security function into an enabler rather than a bottleneck.

The right data management strategy can help democratize access to analytics across an entire team, without the need for a data scientist or data engineer as an intermediary. In 2022, finding specialized talent in data science and data analytics is increasingly difficult due to high demand and the Great Resignation, but complex data management challenges can be alleviated with technology. Enterprise data management is complicated due to multiple formats across various sources, with data warehouses following a schema-on-write approach that limits BI users' ability to interact with data in different ways. Data lakes, on the other hand, store data in its source format and encourage a schema-on-read process model, bypassing the ETL process and enabling faster time to insights. Choosing self-service data analytics platforms can empower business users to access and analyze data on demand without requiring data transformation or movement. These platforms sit on top of cloud-based data repositories, delivering key features that help organizations take advantage of a data lake architecture and activate low-cost cloud object storage. Consolidating certain data into a cloud data platform makes sense for query performance and faster time to insights, especially in distributed architectures like data meshes. Solutions like ChaosSearch can be used to virtually prep data for popular BI tools, democratizing access to clean, normalized, and trustworthy data. Focusing on a sound enterprise data management strategy can help eliminate complexity, freeing up resources for strategic initiatives and empowering employees to use self-service BI or machine learning tools.

ChaosSearch, a cloud data platform provider, strengthened its partnership with AWS in 2021, leveraging various AWS Partner Network programs to deliver value to customers. By expanding its Alliances Team and participating in the AWS ISV Accelerate Program, ChaosSearch gained co-selling support from AWS field sellers and increased sales pipeline growth. The company also achieved AWS Competency Status in Data & Analytics, recognizing its technical expertise in data and analytics. As part of its ongoing engagement with AWS, ChaosSearch plans to deliver AWS training to its teams and participate in numerous AWS events in 2022. This partnership has enabled ChaosSearch to connect with customers who benefit from large-scale search and analytics on their data lake.

ChaosSearch is a platform that offers relational access to log data, enabling users to combine SQL analytics and search querying, unlocking new insights and use cases for both log analytics and business intelligence teams. The platform aims to transform "inexpensive, secure, and durable cloud object storage" into an activated cloud data platform with infinite scale, disruptively low costs, and true multi-model data access. By adding relational capabilities and support for Google Cloud Storage in 2021, ChaosSearch is now poised to unlock powerful new use cases for log users and BI teams, including bringing SQL/relational querying to log analytics users, extending log analytics to business intelligence teams, and enabling multi-dimensional analytics on "your" data lake.