TinyPNG (by Tinify) is a free image optimization service built for developers and designers. It utilizes smart lossy compression to reduce the file sizes of JPEG, PNG, WebP, and AVIF files by up to 80% with no visible quality loss. That means faster load times, better SEO, and lower bandwidth.
You can compress, convert, and resize images via a clean web interface or integrate it into your workflow with the API. The platform also provides an image CDN for fast global delivery of optimized assets. SDKs are available for Python, Node.js, PHP, Java, Ruby, and .NET. WordPress plugin included, plus plenty of community-driven integrations.
No tuning, no noise, Tinify just works. Whether you're optimizing a handful of images or processing millions, it scales effortlessly. All plans include a generous free tier, and support is quick when you need it.
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BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises.
Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.
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Tenzir
Tenzir is a specialized data pipeline engine tailored for security teams, streamlining the processes of collecting, transforming, enriching, and routing security data throughout its entire lifecycle. It allows users to efficiently aggregate information from multiple sources, convert unstructured data into structured formats, and adjust it as necessary. By optimizing data volume and lowering costs, Tenzir also supports alignment with standardized schemas such as OCSF, ASIM, and ECS. Additionally, it guarantees compliance through features like data anonymization and enhances data by incorporating context from threats, assets, and vulnerabilities. With capabilities for real-time detection, it stores data in an efficient Parquet format within object storage systems. Users are empowered to quickly search for and retrieve essential data, as well as to reactivate dormant data into operational status. The design of Tenzir emphasizes flexibility, enabling deployment as code and seamless integration into pre-existing workflows, ultimately seeking to cut SIEM expenses while providing comprehensive control over data management. This approach not only enhances the effectiveness of security operations but also fosters a more streamlined workflow for teams dealing with complex security data.
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Apache Iceberg
Iceberg is an advanced format designed for managing extensive analytical tables efficiently. It combines the dependability and ease of SQL tables with the capabilities required for big data, enabling multiple engines such as Spark, Trino, Flink, Presto, Hive, and Impala to access and manipulate the same tables concurrently without issues. The format allows for versatile SQL operations to incorporate new data, modify existing records, and execute precise deletions. Additionally, Iceberg can optimize read performance by eagerly rewriting data files or utilize delete deltas to facilitate quicker updates. It also streamlines the complex and often error-prone process of generating partition values for table rows while automatically bypassing unnecessary partitions and files. Fast queries do not require extra filtering, and the structure of the table can be adjusted dynamically as data and query patterns evolve, ensuring efficiency and adaptability in data management. This adaptability makes Iceberg an essential tool in modern data workflows.
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