July 2021 Summaries

Coralogix has announced it raised $55 million in Series C funding to enhance its stateful streaming analytics platform, Streamaⓒ, and further its storage-less data vision. This funding will be used to expand the company's R&D teams and platform capabilities, allowing real-time and long-term data analysis without storing the data, thereby addressing challenges associated with the rapid growth of data. Streamaⓒ, which optimizes total cost by allowing customized data routing and storage, has enabled significant savings for large clients, encouraging them to increase data integration for improved observability. Coralogix aims to establish a data-less data platform that provides powerful analytics without concerns about data quotas, retention, or throttling, all while maintaining privacy and compliance. This funding round was led by Greenfield Partners, with continued support from existing investors, and positions Coralogix to aggressively pursue its ambitious goals in the coming quarters.

The article highlights the challenges posed by "tool sprawl" in DevOps teams, where the excessive use of tools leads to inefficiencies, increased technical debt, and reduced productivity. Tool sprawl results in impaired observability, escalated Mean Time To Detection (MTTD) and Mean Time To Repair (MTTR), and increased toil due to constant switching between unintegrated tools. The lack of standardization and data silos further hampers collaboration, creating a toxic work culture. To combat this, the article suggests careful consideration of both functional and non-functional aspects of tools and advocates for the adoption of consolidated platforms like Coralogix, which offer multiple capabilities within a single solution. Moreover, it proposes a three-phase tool consolidation roadmap—plan, prepare, and execute—to streamline toolchains and enhance operational efficiency.

AIOps, or Artificial Intelligence for IT Operations, is a strategy that integrates AI and machine learning into DevOps to enhance observability and system management. Emerging around 2015, AIOps has gained traction as companies recognize its potential to streamline IT operations by automating problem-solving and operational decisions. By leveraging machine learning, AIOps can analyze complex enterprise applications to curate clean data sets, discover patterns, make inferences, and communicate findings effectively, often through visualization and natural language summaries. Companies like William Hill, COTY, and KPN have successfully used AIOps to automate processes, reduce noise, and improve customer experiences. Coralogix, a platform aligned with AIOps principles, enhances these capabilities by analyzing vast data volumes without relying on storage, enabling real-time pattern discovery and dynamic anomaly detection. As IT systems grow increasingly complex, AIOps offers a solution to manage and interpret large data streams, making it accessible even for smaller businesses through various supporting platforms and vendors.

Elasticsearch is a distributed search and analytics engine that facilitates the processing of real-time data, including numerical, geospatial, and structured text, while custom analysis is often required for unstructured text. It utilizes text analysis to make large datasets searchable and actionable, employing analyzers and normalizers to convert text into searchable tokens. Analyzers in Elasticsearch rely on character filters, a tokenizer, and token filters to produce multiple tokens, whereas normalizers generate a single token using character and token filters without a tokenizer. This functionality is particularly useful in applications such as building search engines, log analytics, and storing logs from microservices. Elasticsearch's capabilities are enhanced by tools like Coralogix's Elastic API, which can ingest data for further analysis and provide actionable insights.