Alternatives to Backtrace

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.

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

Seagence's unique execution pathway technology, combined with machine learning, allows you to receive realtime alerts that pinpoint the root cause of any defects in your Java production applications. You can fix your code without any debugging. When you start your application, attach a lightweight runtime Java agent. Seagence agent tracks data about how requests are processed as users access the application. Seagence needs to have enough sample for analysis within 24 hours. Seagence's analytics engine receives the data in realtime. It detects defects and alerts when they occur. Seagence can uncover all defects in your application, even those that are not obvious. Seagence provides defect and root cause information to help you fix your code. Seagence monitors your production application continuously and finds defects and root causes in real-time. This eliminates the need to debug.

Bugfender provides log storage services for developers. It includes remote logger, crash reporter, and in-app feedback. Bugfender records everything that happens in an application, even if it crashes, to help you reproduce and resolve bugs faster and provide better customer service. Bugfender protects the privacy of your user, is network and battery efficient, and keeps logging even when the device is offline. Bugfender can track and destroy bugs before users notice. Bugfender logs every bug on every device and sends the results within seconds. This allows you to find and fix bugs before users even notice. Achieve 5-Star Ratings. Bugfender logs more than just crashes and bugs. Bugfender logs every bit of information you need to build a picture of your users, and earn five-star ratings. Provide exceptional customer service. Our logging tool allows you to target specific users and provide personalized customer service.

IBM Db2 Big SQL is a sophisticated hybrid SQL-on-Hadoop engine that facilitates secure and advanced data querying across a range of enterprise big data sources, such as Hadoop, object storage, and data warehouses. This enterprise-grade engine adheres to ANSI standards and provides massively parallel processing (MPP) capabilities, enhancing the efficiency of data queries. With Db2 Big SQL, users can execute a single database connection or query that spans diverse sources, including Hadoop HDFS, WebHDFS, relational databases, NoSQL databases, and object storage solutions. It offers numerous advantages, including low latency, high performance, robust data security, compatibility with SQL standards, and powerful federation features, enabling both ad hoc and complex queries. Currently, Db2 Big SQL is offered in two distinct variations: one that integrates seamlessly with Cloudera Data Platform and another as a cloud-native service on the IBM Cloud Pak® for Data platform. This versatility allows organizations to access and analyze data effectively, performing queries on both batch and real-time data across various sources, thus streamlining their data operations and decision-making processes. In essence, Db2 Big SQL provides a comprehensive solution for managing and querying extensive datasets in an increasingly complex data landscape.

Trino is a remarkably fast query engine designed to operate at exceptional speeds. It serves as a high-performance, distributed SQL query engine tailored for big data analytics, enabling users to delve into their vast data environments. Constructed for optimal efficiency, Trino excels in low-latency analytics and is extensively utilized by some of the largest enterprises globally to perform queries on exabyte-scale data lakes and enormous data warehouses. It accommodates a variety of scenarios, including interactive ad-hoc analytics, extensive batch queries spanning several hours, and high-throughput applications that require rapid sub-second query responses. Trino adheres to ANSI SQL standards, making it compatible with popular business intelligence tools like R, Tableau, Power BI, and Superset. Moreover, it allows direct querying of data from various sources such as Hadoop, S3, Cassandra, and MySQL, eliminating the need for cumbersome, time-consuming, and error-prone data copying processes. This capability empowers users to access and analyze data from multiple systems seamlessly within a single query. Such versatility makes Trino a powerful asset in today's data-driven landscape.

Regardless of whether your project involves a single table or numerous tables, StarRocks guarantees an impressive performance improvement of at least 300% when compared to other widely used solutions. With its comprehensive array of connectors, you can seamlessly ingest streaming data and capture information in real time, ensuring that you always have access to the latest insights. The query engine is tailored to suit your specific use cases, allowing for adaptable analytics without the need to relocate data or modify SQL queries. This provides an effortless way to scale your analytics capabilities as required. StarRocks not only facilitates a swift transition from data to actionable insights, but also stands out with its unmatched performance, offering a holistic OLAP solution that addresses the most prevalent data analytics requirements. Its advanced memory-and-disk-based caching framework is purpose-built to reduce I/O overhead associated with retrieving data from external storage, significantly enhancing query performance while maintaining efficiency. This unique combination of features ensures that users can maximize their data's potential without unnecessary delays.

Scale from zero to millions of events every second with Arroyo, which is delivered as a single, streamlined binary. It can be run locally on either MacOS or Linux for development purposes and easily deployed to production using Docker or Kubernetes. Arroyo represents a revolutionary approach to stream processing, specifically designed to simplify real-time operations compared to traditional batch processing. From its inception, Arroyo has been crafted so that anyone familiar with SQL can create dependable, efficient, and accurate streaming pipelines. This empowers data scientists and engineers to develop comprehensive real-time applications, models, and dashboards without needing a dedicated team of streaming specialists. Users can perform transformations, filtering, aggregation, and joining of data streams simply by writing SQL, achieving results in under a second. Furthermore, your streaming pipelines shouldn’t trigger alerts just because Kubernetes opted to reschedule your pods. With the capability to operate in contemporary, elastic cloud environments, Arroyo is suitable for everything from basic container runtimes like Fargate to extensive, distributed systems managed by Kubernetes. This versatility makes Arroyo an ideal choice for organizations looking to optimize their streaming data processes.

Digma seamlessly integrates with your IDE, utilizing runtime information to continuously identify issues, regressions, and other problems as you write code. You can instantly observe how any function performs in both continuous integration and production environments, allowing you to detect issues during the development phase. By analyzing code performance, Digma helps expedite code modifications and prevents recurring regressions, offering essential insights on usage, errors, and performance standards to clarify overlooked code and abandoned libraries. You will quickly grasp what is causing any bottlenecks and delays within your code, thanks to critical data such as execution times, scaling challenges, and N+1 query problems, enabling prompt resolutions. Furthermore, incorporating Digma into your GitOps workflow simplifies Pull Request feedback and enhances code review annotations for your team. With Digma, you'll gain the confidence to tackle even the most extensive and intricate projects, making it a vital asset for developers. This tool not only streamlines the coding process but also fosters a deeper understanding of your codebase.

PySpark serves as the Python interface for Apache Spark, enabling the development of Spark applications through Python APIs and offering an interactive shell for data analysis in a distributed setting. In addition to facilitating Python-based development, PySpark encompasses a wide range of Spark functionalities, including Spark SQL, DataFrame support, Streaming capabilities, MLlib for machine learning, and the core features of Spark itself. Spark SQL, a dedicated module within Spark, specializes in structured data processing and introduces a programming abstraction known as DataFrame, functioning also as a distributed SQL query engine. Leveraging the capabilities of Spark, the streaming component allows for the execution of advanced interactive and analytical applications that can process both real-time and historical data, while maintaining the inherent advantages of Spark, such as user-friendliness and robust fault tolerance. Furthermore, PySpark's integration with these features empowers users to handle complex data operations efficiently across various datasets.

Embrace transforms intricate mobile data into actionable insights. By gathering detailed session-level data for every user interaction, Embrace reveals significant insights that drive your growth potential. Propel your ambitions for agile teams and exceptional user experiences with the sole mobile-first data platform designed for your needs. Since mobile technology is at the heart of your business, it is also central to your users' daily lives. The pandemic has only accelerated the shift towards mobile reliance. Choose the only platform and team tailored for your mobile revolution, enabling you to provide seamless user experiences that enhance retention and boost revenue. Embrace automatically highlights the effects of both identified and unidentified issues. You can examine any user’s journey and analyze every technical aspect and user action with a chronological visualization. Swiftly identify the root cause by reproducing any issue on demand, ensuring that you can tackle challenges head-on. Embrace captures only the essential behavioral and technical data necessary to facilitate informed decision-making, allowing you to respond effectively to user needs and enhance overall satisfaction. This focus on precision empowers your organization to thrive in a competitive landscape.

Bugsnag provides comprehensive monitoring of application stability, empowering teams to make informed choices about whether to prioritize the development of new features or to address existing bugs. As a robust full-stack stability monitoring solution tailored for mobile applications, it offers advanced diagnostics that enable you to replicate any error effectively. With a user-friendly interface, you can manage all your applications seamlessly from a single dashboard. Bugsnag serves as a crucial metric for assessing app health, facilitating communication between product and engineering teams. Not every bug requires immediate attention, allowing you to concentrate on those that significantly impact your business. Its extensible libraries come with well-considered defaults and a plethora of customization options. Additionally, the team comprises subject matter experts who are genuinely invested in minimizing errors and ensuring the overall health of your applications, making Bugsnag an invaluable asset for developers.

RubyMotion allows you to swiftly create native applications for iOS, Android, and OS X, utilizing your preferred editor along with the beloved Ruby programming language. It incorporates a statically-compiled variant of Ruby tailored for mobile platforms and offers a command-line extensible toolchain that enables you to personalize your development process to suit your preferences. With RubyMotion, Ruby developers can start building iOS and Android applications immediately. This approach not only enhances the joy and productivity of programmers, but it also facilitates code reuse across platforms, ultimately leading to significant time savings and quicker project deliveries. Moreover, by maintaining a consistent language environment, teams can ensure smoother collaboration and streamlined development practices.

Arm DDT stands out as the premier server and HPC debugger utilized in research, industry, and academia by software engineers and scientists who are developing applications in C++, C, and Fortran that utilize parallel and threaded processes on a range of CPUs, GPUs, Intel, and Arm architectures. This tool is highly regarded for its ability to automatically identify memory issues and divergent behavior, enabling exceptional performance across various scales. It is designed to operate seamlessly across multiple servers and HPC systems, and it also supports native parallel debugging for Python applications. Alongside its industry-leading memory debugging capabilities, Arm DDT offers exceptional support for C++ and complete debugging for Fortran. Additionally, it includes an offline mode suitable for non-interactive debugging, effectively managing and visualizing large data sets. As a robust parallel debugger, Arm DDT can be utilized independently or as part of the Arm Forge debug and profile suite, and its user-friendly graphical interface further enhances its functionality by automatically detecting memory errors and divergent behaviors at all levels of application complexity. This comprehensive tool not only streamlines the debugging process but also significantly contributes to the optimization of scientific and engineering applications.

Apache Spark™ serves as a comprehensive analytics engine designed for extensive data processing tasks. It delivers exceptional performance for both batch and streaming workloads, utilizing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and an efficient physical execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, users can interact with it through various shells, such as Scala, Python, R, and SQL. Spark supports a robust ecosystem of libraries, including SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for real-time data processing, allowing for seamless integration of these libraries within a single application. The platform is versatile, capable of running on multiple environments like Hadoop, Apache Mesos, Kubernetes, standalone setups, or cloud services. Furthermore, it can connect to a wide array of data sources, enabling access to information stored in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other systems, thus providing flexibility to meet various data processing needs. This extensive functionality makes Spark an essential tool for data engineers and analysts alike.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.

PuppyGraph allows you to effortlessly query one or multiple data sources through a cohesive graph model. Traditional graph databases can be costly, require extensive setup time, and necessitate a specialized team to maintain. They often take hours to execute multi-hop queries and encounter difficulties when managing datasets larger than 100GB. Having a separate graph database can complicate your overall architecture due to fragile ETL processes, ultimately leading to increased total cost of ownership (TCO). With PuppyGraph, you can connect to any data source, regardless of its location, enabling cross-cloud and cross-region graph analytics without the need for intricate ETLs or data duplication. By directly linking to your data warehouses and lakes, PuppyGraph allows you to query your data as a graph without the burden of constructing and maintaining lengthy ETL pipelines typical of conventional graph database configurations. There's no longer a need to deal with delays in data access or unreliable ETL operations. Additionally, PuppyGraph resolves scalability challenges associated with graphs by decoupling computation from storage, allowing for more efficient data handling. This innovative approach not only enhances performance but also simplifies your data management strategy.

A parallel query engine facilitates access to time- and symbol-indexed data efficiently. It features an enhanced SQL syntax that allows for sophisticated filtering and comprehensive aggregations. This system consolidates various types of financial information, such as quotes, trades, snapshots, and reference data, into a single repository. Users can conduct strategy backtesting utilizing high-frequency data and engage in quantitative research as well as market microstructure analysis. The platform provides detailed transaction cost analysis and allows for rollup reporting, ensuring thorough insight into trading activities. It also includes market surveillance capabilities and tools for detecting anomalies. Moreover, it can decompose non-transparent ETFs and ETNs, utilizing FAST, SBE, and proprietary protocols for improved performance. A plain text protocol ensures ease of use, while both consolidated and direct feeds are available for data ingestion. Built-in tools for monitoring latency are included, along with comprehensive end-of-day archives. The engine supports ETL processes from both institutional and retail financial data sources. It boasts a parallel SQL engine with syntax extensions, allowing advanced filtering by various criteria such as trading session and auction stage. Additionally, it offers optimized aggregate calculations for OHLCV and VWAP metrics. An interactive SQL console equipped with auto-completion enhances user experience, and an API endpoint facilitates programmatic integration. Scheduled SQL reporting is available with options for delivery via email, file, or web, along with JDBC and ODBC drivers for broader accessibility. This robust system is designed to meet the demands of modern financial analysis and trading strategies.

Debugging should be straightforward, and AQTime Pro transforms intricate memory and performance data into clear, actionable insights, allowing for rapid identification of bugs and their underlying causes. While the process of locating and resolving unique bugs can often be laborious and complex, AQTime Pro simplifies this task significantly. With a suite of over a dozen profilers, it enables you to detect memory leaks, performance issues, and code coverage deficiencies with just a few clicks. This powerful tool empowers developers to eliminate all types of bugs efficiently, helping them return their focus to producing high-quality code. Don’t let code profiling tools limit you to a single codebase or framework, which can hinder your ability to uncover performance issues, memory leaks, and code coverage gaps specific to your project. AQTime Pro stands out as the versatile solution that can be employed across various codebases and frameworks within a single project. Its extensive language support includes popular programming languages such as C/C++, Delphi, .NET, Java, and more, making it an invaluable asset for diverse development environments. With AQTime Pro at your disposal, you can streamline your debugging process and enhance your coding efficiency like never before.

Dynamic and engaging visualizations enable the discovery of trends within user and business processes, offering comprehensive insight into the foundational computations. A concise collection of sequential operations delivers extensive functionality and meticulous control, all achievable in fewer than ten lines of code. An adaptive query engine allows users to effortlessly balance the trade-offs between query accuracy, processing speed, and costs to suit their specific requirements. Currently, Motif employs a specialized domain-specific language known as Sequence Operations Language (SOL), which we find to be more intuitive than SQL while providing greater capabilities than a simple drag-and-drop interface. Additionally, we have developed a bespoke engine designed to enhance the efficiency of sequence queries, while strategically sacrificing unnecessary precision that does not contribute to decision-making, in favor of improving query performance. This approach not only streamlines the user experience but also maximizes the effectiveness of data analysis.

Presto serves as an open-source distributed SQL query engine designed for executing interactive analytic queries across data sources that can range in size from gigabytes to petabytes. It addresses the challenges faced by data engineers who often navigate multiple query languages and interfaces tied to isolated databases and storage systems. Presto stands out as a quick and dependable solution by offering a unified ANSI SQL interface for comprehensive data analytics and your open lakehouse. Relying on different engines for various workloads often leads to the necessity of re-platforming in the future. However, with Presto, you benefit from a singular, familiar ANSI SQL language and one engine for all your analytic needs, negating the need to transition to another lakehouse engine. Additionally, it efficiently accommodates both interactive and batch workloads, handling small to large datasets and scaling from just a few users to thousands. By providing a straightforward ANSI SQL interface for all your data residing in varied siloed systems, Presto effectively integrates your entire data ecosystem, fostering seamless collaboration and accessibility across platforms. Ultimately, this integration empowers organizations to make more informed decisions based on a comprehensive view of their data landscape.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.

Tabular is an innovative open table storage solution designed by the same team behind Apache Iceberg, allowing seamless integration with various computing engines and frameworks. By leveraging this technology, users can significantly reduce both query times and storage expenses, achieving savings of up to 50%. It centralizes the enforcement of role-based access control (RBAC) policies, ensuring data security is consistently maintained. The platform is compatible with multiple query engines and frameworks, such as Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python, offering extensive flexibility. With features like intelligent compaction and clustering, as well as other automated data services, Tabular further enhances efficiency by minimizing storage costs and speeding up query performance. It allows for unified data access at various levels, whether at the database or table. Additionally, managing RBAC controls is straightforward, ensuring that security measures are not only consistent but also easily auditable. Tabular excels in usability, providing robust ingestion capabilities and performance, all while maintaining effective RBAC management. Ultimately, it empowers users to select from a variety of top-tier compute engines, each tailored to their specific strengths, while also enabling precise privilege assignments at the database, table, or even column level. This combination of features makes Tabular a powerful tool for modern data management.

Qubole stands out as a straightforward, accessible, and secure Data Lake Platform tailored for machine learning, streaming, and ad-hoc analysis. Our comprehensive platform streamlines the execution of Data pipelines, Streaming Analytics, and Machine Learning tasks across any cloud environment, significantly minimizing both time and effort. No other solution matches the openness and versatility in handling data workloads that Qubole provides, all while achieving a reduction in cloud data lake expenses by more than 50 percent. By enabling quicker access to extensive petabytes of secure, reliable, and trustworthy datasets, we empower users to work with both structured and unstructured data for Analytics and Machine Learning purposes. Users can efficiently perform ETL processes, analytics, and AI/ML tasks in a seamless workflow, utilizing top-tier open-source engines along with a variety of formats, libraries, and programming languages tailored to their data's volume, diversity, service level agreements (SLAs), and organizational regulations. This adaptability ensures that Qubole remains a preferred choice for organizations aiming to optimize their data management strategies while leveraging the latest technological advancements.

Starburst empowers organizations to enhance their decision-making capabilities by providing rapid access to all their data without the hassle of transferring or duplicating it. As companies accumulate vast amounts of data, their analysis teams often find themselves waiting for access to perform their evaluations. By facilitating direct access to data at its source, Starburst ensures that teams can quickly and accurately analyze larger datasets without the need for data movement. Starburst Enterprise offers a robust, enterprise-grade version of the open-source Trino (formerly known as Presto® SQL), which is fully supported and tested for production use. This solution not only boosts performance and security but also simplifies the deployment, connection, and management of a Trino environment. By enabling connections to any data source—be it on-premises, in the cloud, or within a hybrid cloud setup—Starburst allows teams to utilize their preferred analytics tools while seamlessly accessing data stored in various locations. This innovative approach significantly reduces the time taken for insights, helping businesses stay competitive in a data-driven world.

Polars offers a comprehensive Python API that reflects common data wrangling practices, providing a wide array of functionalities for manipulating DataFrames through an expression language that enables the creation of both efficient and clear code. Developed in Rust, Polars makes deliberate choices to ensure a robust DataFrame API that caters to the Rust ecosystem's needs. It serves not only as a library for DataFrames but also as a powerful backend query engine for your data models, allowing for versatility in data handling and analysis. This flexibility makes it a valuable tool for data scientists and engineers alike.

If you are looking to provide your clients with online access to their LIMS data and reports, AIS labPortal can help you achieve that goal seamlessly. There is no need to mail paper copies of sample analyses to customers anymore. With a unique login and secure password, clients can conveniently retrieve their data from any computer, making the process not only safer and more efficient but also environmentally sustainable. labPortal serves as a secure, cloud-based platform where clients can quickly access their sample information from their desktop, tablet, or smartphone. The user-friendly 'inbox' style interface features an advanced query engine, conditional highlighting, and the option to export data to Microsoft Excel. Additionally, the software includes a straightforward sample registration form, enabling users to pre-register samples online with ease. Eliminating the need for manual data transcription saves valuable time and reduces the potential for errors in reporting. Overall, AIS labPortal offers a modern solution to streamline data access and enhance client satisfaction.

Amazon Timestream is a rapid, scalable, and serverless database service designed for time series data, catering to IoT and operational applications, allowing users to store and analyze trillions of events daily at speeds up to 1,000 times faster and at costs as low as one-tenth of traditional relational databases. By efficiently managing the lifecycle of time series data, Amazon Timestream keeps current data in memory while transferring older data to a more economical storage tier based on user-defined policies, thus saving both time and expenses. Its unique query engine enables seamless access to and analysis of both recent and historical data without the need for users to specify whether the data is stored in the in-memory or cost-effective tier. Additionally, Amazon Timestream comes equipped with built-in time series analytics functions, allowing users to detect trends and patterns in their data almost in real-time, enhancing decision-making processes. This combination of features makes Timestream an optimal choice for businesses looking to leverage time series data efficiently.

Elevate your log management with Honeycomb, a platform designed specifically for contemporary development teams aiming to gain insights into application performance while enhancing log management capabilities. With Honeycomb’s rapid query functionality, you can uncover hidden issues across your system’s logs, metrics, and traces, utilizing interactive charts that provide an in-depth analysis of raw data that boasts high cardinality. You can set up Service Level Objectives (SLOs) that reflect user priorities, which helps in reducing unnecessary alerts and allows you to focus on what truly matters. By minimizing on-call responsibilities and speeding up code deployment, you can ensure customer satisfaction remains high. Identify the root causes of performance issues, optimize your code efficiently, and view your production environment in high resolution. Our SLOs will alert you when customers experience difficulties, enabling you to swiftly investigate the underlying problems—all from a single interface. Additionally, the Query Builder empowers you to dissect your data effortlessly, allowing you to visualize behavioral trends for both individual users and services, organized by various dimensions for enhanced analytical insights. This comprehensive approach ensures that your team can respond proactively to performance challenges while refining the overall user experience.

Amazon Athena serves as an interactive query service that simplifies the process of analyzing data stored in Amazon S3 through the use of standard SQL. As a serverless service, it eliminates the need for infrastructure management, allowing users to pay solely for the queries they execute. The user-friendly interface enables you to simply point to your data in Amazon S3, establish the schema, and begin querying with standard SQL commands, with most results returning in mere seconds. Athena negates the requirement for intricate ETL processes to prepare data for analysis, making it accessible for anyone possessing SQL skills to swiftly examine large datasets. Additionally, Athena integrates seamlessly with AWS Glue Data Catalog, which facilitates the creation of a consolidated metadata repository across multiple services. This integration allows users to crawl data sources to identify schemas, update the Catalog with new and modified table and partition definitions, and manage schema versioning effectively. Not only does this streamline data management, but it also enhances the overall efficiency of data analysis within the AWS ecosystem.

With Bugsee, you can access videos, network information, and logs that pinpoint the origins of bugs and crashes occurring in live applications, eliminating the need to replicate sporadic issues. This platform ensures that all vital data is readily available, allowing you to understand precisely what caused unexpected application behavior. You can review videos documenting user interactions, backend communications, and system states associated with the incidents. Additionally, Bugsee provides statistics on recurrent crashes and analyzes trends categorized by device type, operating system version, and time. You'll receive detailed information such as the exact filename, method, and line number at which the crash occurred, along with the states of all other threads active during the incident. Furthermore, it captures all HTTP and HTTPS requests and responses—complete with headers and body—associated with your app, enabling you to replay console logs in sync with the recorded video and network activity for a comprehensive examination. This holistic view of your app's performance aids in quicker resolutions and enhances overall stability.

Proactively discover, predict, and resolve errors with the continuous code improvement platform.

Open-source and free to use, this tool is compatible across various platforms and caters to all EVM blockchains. The Solidity Debugger Pro (sdbg) is an extension for VS Code that enhances the debugging experience for Solidity projects with a plethora of features. It accommodates all EVM-compatible blockchains, making it simple for developers to debug their smart contracts either locally or through a forked node. Additionally, sdbg offers integrated debugging support specifically designed for the widely-used Hardhat framework, ensuring a seamless development process. With its robust capabilities, sdbg significantly improves the efficiency of debugging in Solidity projects.

Errsole is a free open-source logger for Node.js applications. It comes with a built-in log viewer to view, filter, and search your application logs. 1) Minimal Setup: Just include the Errsole package in your code—no need for dedicated servers, software installations, or complicated configurations. 2) Logger++: Errsole automatically collects all logs from the Node.js console. Additionally, it provides advanced logging functions that support multiple log levels and the ability to attach metadata to logs. 3) Store Anywhere: Store your logs wherever you want—whether in a file or any database of your choice. You can also configure log rotation to specify how long logs should be retained. 4) Log Viewer: View, filter, and search through your logs using the built-in Web Dashboard. Secure authentication and team management features ensure that only you and your team can access the logs. 5) Critical Error Notifications: Get immediate notifications when your app crashes or encounters critical errors. The notification includes the error message, the app name, the environment, the server name, and a direct link to view the error in your logs.

Enhance both your production and staging environments by integrating logs, metrics, and traces in real-time and on-demand directly from your IDE or command line interface. With Lightrun, you can significantly improve productivity and achieve complete code-level visibility. You can add logs and metrics instantly while services are operational, making it easier to debug complex architectures like monoliths, microservices, Kubernetes, Docker Swarm, ECS, and serverless applications. Quickly insert any missing log lines, instrument necessary metrics, or establish snapshots as needed without the hassle of recreating the production setup or redeploying. When you invoke instrumentation, the resulting data gets sent to your log analysis platform, IDE, or preferred APM tool. This allows for thorough analysis of code behavior to identify bottlenecks and errors without interrupting the running application. You can seamlessly incorporate extensive logs, snapshots, counters, timers, function durations, and much more without risking system stability. This streamlined approach lets you focus on coding rather than getting bogged down in debugging, eliminating the need for constant restarts or redeployments when troubleshooting. Ultimately, this results in a more efficient development workflow, allowing you to maintain momentum on your projects.

Xdebug is a powerful PHP extension that enhances the development workflow by offering various tools and functionalities. It allows developers to step through code in their integrated development environment as scripts run, making debugging much easier. The extension provides an enhanced version of the var_dump() function and delivers stack traces for notices, warnings, errors, and exceptions, clearly indicating the path leading to issues. Additionally, it logs all function calls, including arguments and their locations, to the disk, and can be configured to also record every variable assignment and return value for each function. This feature set enables developers, with the aid of visualization tools, to thoroughly examine the performance of their PHP applications and identify any bottlenecks. Moreover, Xdebug reveals the sections of code that are executed during unit testing with PHPUnit, aiding in better test coverage. For convenience, installing Xdebug via a package manager is typically the quickest method; simply replace the PHP version with the version you are currently using. You can also install Xdebug using PECL on both Linux and macOS, utilizing Homebrew for a streamlined setup process. Overall, Xdebug significantly enhances PHP development by providing essential debugging tools and performance insights.

Elevate your debugging and web performance skills with an advanced in-browser HTTP sniffer that allows you to analyze network traffic produced by a web page without the need to switch to an external tool. This tool enables precise measurement of a web page's network performance while uncovering potential enhancements to its speed, all without needing additional configurations or proxies—even for encrypted HTTPS traffic! Instantly identify vulnerable SSL configurations and other security-related concerns on your web server. The free Basic Edition allows anyone to send you comprehensive log files, facilitating remote error diagnosis and performance troubleshooting. Furthermore, the HttpWatch API empowers you to gather performance metrics from your automated website testing. Fully compatible with Chrome, Edge, and Internet Explorer, HttpWatch displays the HTTP and HTTPS traffic generated during web page access, and by selecting a request, you can view all relevant information conveniently organized in a tabbed interface. This seamless integration ensures that you have all the necessary insights at your fingertips to optimize your web applications.

Unexpected outcomes are a common occurrence in software development. With complete insight into the entire sequence of calls, developers can pinpoint the origins of errors and unexpected results in real time with remarkable accuracy. The discipline of software engineering heavily depends on unit testing to create efficient and production-ready software solutions. LangSmith offers similar capabilities tailored specifically for LLM applications. You can quickly generate test datasets, execute your applications on them, and analyze the results without leaving the LangSmith platform. This tool provides essential observability for mission-critical applications with minimal coding effort. LangSmith is crafted to empower developers in navigating the complexities and leveraging the potential of LLMs. We aim to do more than just create tools; we are dedicated to establishing reliable best practices for developers. You can confidently build and deploy LLM applications, backed by comprehensive application usage statistics. This includes gathering feedback, filtering traces, measuring costs and performance, curating datasets, comparing chain efficiencies, utilizing AI-assisted evaluations, and embracing industry-leading practices to enhance your development process. This holistic approach ensures that developers are well-equipped to handle the challenges of LLM integrations.

Shoreline is the only cloud reliability platform that allows DevOps engineers to build automations in a matter of minutes and fix problems forever. Shoreline’s modern “Operations at the Edge” architecture runs efficient agents in the background of all monitored hosts. Agents run as a DaemonSet on Kubernetes or an installed package on VMs (apt, yum). The Shoreline backend is hosted by Shoreline in AWS, or deployed in your AWS virtual private cloud. Debugging and repairing issues is easy with advanced tooling for your best SREs, Jupyter style notebooks for the broader team, and a platform that makes building automations 30X faster by allowing operators to manage their entire fleet as if it were a single box. Shoreline does the heavy lifting, setting up monitors and building repair scripts, so that customers only need to configure them for their environment.

Polar Signals Cloud is a continuous profiling solution that operates continuously without the need for instrumentation, aimed at enhancing performance, comprehending incidents, and reducing infrastructure expenses. By executing a single command and following an exceptionally straightforward onboarding guide, you can commence cost savings and performance optimization for your infrastructure almost instantly. The capability to analyze past incidents allows users to trace and address issues effectively. The profiling data generated offers unparalleled insights into the execution of processes over time, enabling you to confidently identify the most critical areas for optimization based on statistical analysis. Many companies find that 20-30% of their resources are squandered on inefficient code paths that could be readily improved. By utilizing a remarkable combination of technologies, Polar Signals Cloud provides a profiling toolkit that meets the demands of modern infrastructure and applications. Thanks to its zero-instrumentation approach, you can implement it right away and start benefiting from actionable observability data that drives better decision-making. With ongoing use, organizations can continually refine their performance strategies and maximize resource allocation.

A tool designed for developers to oversee and enhance AI functionalities driven by LLMs and various foundational models. Our user-friendly SDK provides a clear and actionable perspective on the performance of your generative AI applications in real-time. Seamlessly incorporate LLM management into your current code architecture and developer processes. Leverage detailed access controls and comprehensive audit logs to ensure complete oversight of your data. Gain valuable insights on optimizing user interactions with LLMs. Developer teams are not only uniquely qualified to embed these advanced features into existing software solutions, but their inclination to deploy, refine, and enhance will become increasingly critical in the future. As software continues to evolve and adapt, we anticipate that engineering teams will play a pivotal role in transforming this adaptability into engaging and highly personalized user experiences. Ultimately, developers will find themselves at the forefront of the generative AI transformation, shaping its future trajectory.

If you’ve ever taken a screenshot or recorded your screen, you’re already familiar with how to use Jam. Jam magically generates comprehensive bug reports containing all the essential information engineers require. It enhances bug reporting in any tool you prefer to utilize. You can easily share links to your bug reports or send them directly to your chosen issue tracker. Your teammates, regardless of whether they have signed up or not, can benefit from this tool. Jam gathers everything necessary for engineers to effectively troubleshoot issues. We have eliminated the need for those tedious follow-up calls to provide additional context, as all the required information is readily accessible. With Jam, you get perfectly crafted bug reports every time, minimizing unnecessary back and forth. It integrates seamlessly with your preferred issue trackers and tools, making it as straightforward as taking a screenshot. Trusted by countless teams across various companies, you can report bugs in mere seconds without interrupting your workflow. Currently, Jam assists developers during the code review and testing phases by capturing crucial bug diagnostic data and instantly replaying the issue, making it easier to pinpoint what needs fixing. This innovative approach not only saves time but also enhances collaboration among team members.

QuasarDB, the core of Quasar's intelligence, is an advanced, distributed, column-oriented database management system specifically engineered for high-performance timeseries data handling, enabling real-time processing for massive petascale applications. It boasts up to 20 times less disk space requirement, making it exceptionally efficient. The unmatched ingestion and compression features of QuasarDB allow for up to 10,000 times quicker feature extraction. This database can perform real-time feature extraction directly from raw data via an integrated map/reduce query engine, a sophisticated aggregation engine that utilizes SIMD capabilities of contemporary CPUs, and stochastic indexes that consume minimal disk storage. Its ultra-efficient resource utilization, ability to integrate with object storage solutions like S3, innovative compression methods, and reasonable pricing structure make it the most economical timeseries solution available. Furthermore, QuasarDB is versatile enough to operate seamlessly across various platforms, from 32-bit ARM devices to high-performance Intel servers, accommodating both Edge Computing environments and traditional cloud or on-premises deployments. Its scalability and efficiency make it an ideal choice for businesses aiming to harness the full potential of their data in real-time.

VeloDB, which utilizes Apache Doris, represents a cutting-edge data warehouse designed for rapid analytics on large-scale real-time data. It features both push-based micro-batch and pull-based streaming data ingestion that occurs in mere seconds, alongside a storage engine capable of real-time upserts, appends, and pre-aggregations. The platform delivers exceptional performance for real-time data serving and allows for dynamic interactive ad-hoc queries. VeloDB accommodates not only structured data but also semi-structured formats, supporting both real-time analytics and batch processing capabilities. Moreover, it functions as a federated query engine, enabling seamless access to external data lakes and databases in addition to internal data. The system is designed for distribution, ensuring linear scalability. Users can deploy it on-premises or as a cloud service, allowing for adaptable resource allocation based on workload demands, whether through separation or integration of storage and compute resources. Leveraging the strengths of open-source Apache Doris, VeloDB supports the MySQL protocol and various functions, allowing for straightforward integration with a wide range of data tools, ensuring flexibility and compatibility across different environments.

ClickHouse is an efficient, open-source OLAP database management system designed for high-speed data processing. Its column-oriented architecture facilitates the creation of analytical reports through real-time SQL queries. In terms of performance, ClickHouse outshines similar column-oriented database systems currently on the market. It has the capability to handle hundreds of millions to over a billion rows, as well as tens of gigabytes of data, on a single server per second. By maximizing the use of available hardware, ClickHouse ensures rapid query execution. The peak processing capacity for individual queries can exceed 2 terabytes per second, considering only the utilized columns after decompression. In a distributed environment, read operations are automatically optimized across available replicas to minimize latency. Additionally, ClickHouse features multi-master asynchronous replication, enabling deployment across various data centers. Each node operates equally, effectively eliminating potential single points of failure and enhancing overall reliability. This robust architecture allows organizations to maintain high availability and performance even under heavy workloads.

Palo empowers businesses to establish a PB-level MPP architecture for their data warehouse in just a few minutes while seamlessly importing vast amounts of data from sources such as RDS, BOS, and BMR. This capability allows Palo to conduct multi-dimensional analyses on big data effectively. Furthermore, Palo is designed to work harmoniously with leading BI tools, enabling data analysts to visually interpret and swiftly derive insights from the data, thereby enhancing decision-making processes. Boasting an industry-leading MPP query engine, it incorporates features like column storage, intelligent indexing, and vector execution capabilities. Additionally, it offers in-library analytics, window functions, and various advanced analytical tools, allowing users to create materialized views and alter table structures without any service interruption. With its robust support for flexible and efficient data recovery, Palo stands out as a powerful solution for enterprises aiming to leverage their data effectively. This comprehensive suite of features makes it easier for organizations to optimize their data strategies and drive innovation.

With your data now actively flowing, it's essential to extract meaningful insights from it. Stream processing allows for immediate analysis of your data streams, though establishing the necessary infrastructure can be a daunting task. To address this challenge, Confluent has introduced ksqlDB, a database specifically designed for applications that require stream processing. By continuously processing data streams generated across your organization, you can turn your data into actionable insights right away. ksqlDB features an easy-to-use syntax that facilitates quick access to and enhancement of data within Kafka, empowering development teams to create real-time customer experiences and meet operational demands driven by data. This platform provides a comprehensive solution for gathering data streams, enriching them, and executing queries on newly derived streams and tables. As a result, you will have fewer infrastructure components to deploy, manage, scale, and secure. By minimizing the complexity in your data architecture, you can concentrate more on fostering innovation and less on technical maintenance. Ultimately, ksqlDB transforms the way businesses leverage their data for growth and efficiency.

LlamaIndex serves as a versatile "data framework" designed to assist in the development of applications powered by large language models (LLMs). It enables the integration of semi-structured data from various APIs, including Slack, Salesforce, and Notion. This straightforward yet adaptable framework facilitates the connection of custom data sources to LLMs, enhancing the capabilities of your applications with essential data tools. By linking your existing data formats—such as APIs, PDFs, documents, and SQL databases—you can effectively utilize them within your LLM applications. Furthermore, you can store and index your data for various applications, ensuring seamless integration with downstream vector storage and database services. LlamaIndex also offers a query interface that allows users to input any prompt related to their data, yielding responses that are enriched with knowledge. It allows for the connection of unstructured data sources, including documents, raw text files, PDFs, videos, and images, while also making it simple to incorporate structured data from sources like Excel or SQL. Additionally, LlamaIndex provides methods for organizing your data through indices and graphs, making it more accessible for use with LLMs, thereby enhancing the overall user experience and expanding the potential applications.

Apache Hive is a data warehousing solution that enables users to read, write, and manage extensive datasets stored across distributed systems utilizing SQL. It allows for the imposition of structure on existing stored data. Users can connect with Hive through a command line interface and a JDBC driver. As an open-source initiative, Apache Hive is maintained by dedicated volunteers at the Apache Software Foundation. Initially, it was part of the Apache® Hadoop® ecosystem but has since evolved into a standalone top-level project. We invite those interested to explore the project further and share their skills. To run SQL applications and queries on distributed datasets, traditional SQL queries need to be executed via the MapReduce Java API. However, Hive simplifies this process by offering a SQL abstraction that allows users to execute SQL-like queries known as HiveQL, without requiring the implementation of low-level Java API queries. This makes working with large datasets more accessible and efficient for users familiar with SQL.

Impala delivers rapid response times and accommodates a high number of concurrent users for business intelligence and analytical queries within the Hadoop ecosystem, supporting frameworks like Iceberg, various open data formats, and numerous cloud storage solutions. It is designed to scale seamlessly, even in environments that host multiple tenants. Additionally, Impala integrates with native Hadoop security protocols and utilizes Kerberos for authentication, while the Ranger module allows for precise user and application authorization based on the data they need to access. This means you can leverage the same file formats, data structures, security measures, and resource management systems as your existing Hadoop setup, eliminating the need for redundant infrastructure or unnecessary data transformations. For those already using Apache Hive, Impala is compatible, sharing the same metadata and ODBC driver, which streamlines the transition. Just like Hive, Impala employs SQL, thereby alleviating the need to develop new implementations. With Impala, a greater number of users can engage with a wider array of data via a unified repository, ensuring that valuable insights are accessible from the source to analysis without compromising on efficiency. Ultimately, this makes Impala an essential tool for organizations looking to enhance their data interaction capabilities.