Alternatives to Backtrace

ANSI SQL allows you to analyze petabytes worth of data at lightning-fast speeds with no operational overhead. Analytics at scale with 26%-34% less three-year TCO than cloud-based data warehouse alternatives. You can unleash your insights with a trusted platform that is more secure and scales with you. Multi-cloud analytics solutions that allow you to gain insights from all types of data. You can query streaming data in real-time and get the most current information about all your business processes. Machine learning is built-in and allows you to predict business outcomes quickly without having to move data. With just a few clicks, you can securely access and share the analytical insights within your organization. Easy creation of stunning dashboards and reports using popular business intelligence tools right out of the box. BigQuery's strong security, governance, and reliability controls ensure high availability and a 99.9% uptime SLA. Encrypt your data by default and with customer-managed encryption keys

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.

Trino is an engine that runs at incredible speeds. Fast-distributed SQL engine for big data analytics. Helps you explore the data universe. Trino is an extremely parallel and distributed query-engine, which is built from scratch for efficient, low latency analytics. Trino is used by the largest organizations to query data lakes with exabytes of data and massive data warehouses. Supports a wide range of use cases including interactive ad-hoc analysis, large batch queries that take hours to complete, and high volume apps that execute sub-second queries. Trino is a ANSI SQL query engine that works with BI Tools such as R Tableau Power BI Superset and many others. You can natively search data in Hadoop S3, Cassandra MySQL and many other systems without having to use complex, slow and error-prone copying processes. Access data from multiple systems in a single query.

Errsole is a bug-tracking solution that helps streamline logging and debugging for Node.js live apps. The platform offers various functionalities such as error tracking, slow request logging, and centralized logging. Errsole also provides real-time error notifications and daily summaries via Email or Slack. Additionally, the solution helps developers debug live applications directly from their web browsers. - Centralized Logging: Errsole centralizes all application logs from servers in one place. - Error Tracking: Errsole centralizes all application errors in one place for viewing and resolution. - Root Cause Analysis: With Errsole, developers can pinpoint the exact HTTP requests that caused errors. - Slow Request Logging: Errsole tracks and records slow HTTP requests in the application, enabling users to pinpoint and address performance bottlenecks. - Debugging: With Errsole Debugger, developers can debug live applications directly from their web browser. - Collaboration: Invite developers to the app, manage their permissions, and assign errors to individual developers.

Scale from 0 to millions of events every second. Arroyo is shipped as a single compact binary. Run locally on MacOS, Linux or Kubernetes for development and deploy to production using Docker or Kubernetes. Arroyo is an entirely new stream processing engine that was built from the ground-up to make real time easier than batch. Arroyo has been designed so that anyone with SQL knowledge can build reliable, efficient and correct streaming pipelines. Data scientists and engineers are able to build real-time dashboards, models, and applications from end-to-end without the need for a separate streaming expert team. SQL allows you to transform, filter, aggregate and join data streams with results that are sub-second. Your streaming pipelines should not page someone because Kubernetes rescheduled your pods. Arroyo can run in a modern, elastic cloud environment, from simple container runtimes such as Fargate, to large, distributed deployments using the Kubernetes logo.

A hybrid SQL-onHadoop engine that delivers advanced, security-rich data queries across enterprise big data sources including Hadoop object storage and data warehouses. IBM Db2 Big SQL, an enterprise-grade, hybrid ANSI compliant SQL-on-Hadoop engine that delivers massively parallel processing and advanced data query, is available. Db2 Big SQL allows you to connect to multiple sources, such as Hadoop HDFS and WebHDFS. RDMS, NoSQL database, object stores, and RDMS. You can benefit from low latency, high speed, data security, SQL compatibility and federation capabilities to perform complex and ad-hoc queries. Db2 Big SQL now comes in two versions. It can be integrated with Cloudera Data Platform or accessed as a cloud native service on the IBM Cloud Pak®. for Data platform. Access, analyze, and perform queries on real-time and batch data from multiple sources, including Hadoop, object stores, and data warehouses.

PySpark is a Python interface for Apache Spark. It allows you to create Spark applications using Python APIs. Additionally, it provides the PySpark shell that allows you to interactively analyze your data in a distributed environment. PySpark supports Spark's most popular features, including Spark SQL, DataFrame and Streaming. Spark SQL is a Spark module that allows structured data processing. It can be used as a distributed SQL query engine and a programming abstraction called DataFrame. The streaming feature in Apache Spark, which runs on top of Spark allows for powerful interactive and analytic applications across streaming and historical data. It also inherits Spark's ease-of-use and fault tolerance characteristics.

StarRocks offers at least 300% more performance than other popular solutions, whether you're using a single or multiple tables. With a rich set connectors, you can ingest real-time data into StarRocks for the latest insights. A query engine that adapts your use cases. StarRocks allows you to scale your analytics easily without moving your data or rewriting SQL. StarRocks allows a rapid journey between data and insight. StarRocks is unmatched in performance and offers a unified OLAP system that covers the most common data analytics scenarios. StarRocks offers at least 300% faster performance than other popular solutions, whether you are working with one table or many. StarRocks' built-in memory-and-disk-based caching framework is specifically designed to minimize the I/O overhead of fetching data from external storage to accelerate query performance.

Bugsnag monitors your application stability, allowing you to make data-driven decisions about whether you should be building new features or fixing bugs. We provide a full-stack stability monitoring solution that offers best-in-class functionality to mobile applications. Rich diagnostics that help you reproduce any error. All your apps can be accessed from one dashboard. It's a simple, thoughtful user experience. The most important metric for app health -- the common language between product and engineering teams. Not all bugs are worth fixing. You should only fix the ones that are important to your business. You have many customization options and extensive libraries with opinionated defaults. Experts who care deeply about the health and error reduction of your apps.

Digma integrates with your IDE and uses runtime information to highlight issues, regressions and problems as you code. Identify issues in development by seeing how a function scales up or down in CI and production. Digma helps you to accelerate code changes and avoid regressions by analyzing the code's performance. Digma also provides critical analytics about usage, errors and performance baselines. Understand what's causing your code to slow down and bottleneck. You can fix problems quickly with valuable data, such as code execution time, scaling limitations, or N+1 query issues. When your team integrates Digma in your GitOps cycles, Pull Request feedback and annotation of code becomes much easier. Digma allows you to understand it and begin working on it without fear - no matter what size or complexity it is.

Arm DDT is the most widely used server and HPC debugger in academia, research, and industry for software engineers and scientists who develop C++, C, Fortran parallel, and threaded programs on CPUs and GPUs, Intel and Arm. Arm DDT is trusted for its ability to detect memory bugs and divergent behavior, enabling it to deliver lightning-fast performance on all scales. Cross-platform support for multiple servers and HPC architectures. Native parallel debugging for Python applications. Market-leading memory debugging. Outstanding C++ debugging support. Complete Fortran debugging support. Offline mode allows you to debug non-interactively. Large data sets can be visualized and handled. Arm DDT is a powerful parallel tool that can be used as a standalone debugger or as part the Arm Forge profile and debug suite. Its intuitive interface graphically allows for automatic detection of memory bugs at all scales and divergent behavior.

Embrace turns complex mobile data into action. Embrace provides powerful insights for your growth by collecting detailed session-level data from every user experience. The only mobile-first data platform that enables you to achieve high-velocity teams, optimal user experiences, and other goals. Mobile is the heart of your business, because mobile is the heart of your users' lives. Covid has only helped to accelerate the mobile eventuality. Choose the only platform and team that is built for mobile transformation. This will allow you to deliver flawless user experiences that increase retention and revenue. Recognize the impact of both known issues and unknown issues. Avoid costly investigations that are based on guesswork and instead focus on troubleshooting with complete data.

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.

RubyMotion allows you to quickly create cross-platform native apps on iOS, Android, and OS X using your favorite editor and the amazing Ruby language. RubyMotion includes a statically-compiled Ruby language for native mobile platforms. It also features a command-line-based extensible toolchain that allows you to customize your development workflow as you wish. RubyMotion allows Ruby developers to create iOS and Android apps today. Ruby makes programmers happy, productive, and you can reuse code across platforms to save time and ship faster.

Parallel query engine with symbol- and time-indexed data access. Extended SQL syntax with advanced filtering, aggregations and more. Consolidate all quotes, trades and snapshots in one place. Strategy backtesting using high-frequency data. Quantitative and market microstructure analysis. Granular transaction cost analysis and rollup report. Market surveillance and anomaly detection. Non-transparent ETF/ETN decomposition. FAST, SBE and proprietary protocols. Plain text protocol. Consolidated and direct feeds. Built-in latency monitoring tools. End-of-day archives. ETL from retail and institutional financial data platforms. Parallel SQL engine with syntax extensions. Advanced filtering via trading session, auction stage, and index composition. Optimized aggregates to OHLCV and VWAP calculations. Interactive SQL console with auto completion. API endpoint for programmatic integrtion. Scheduled SQL reporting via email, file, or web delivery. JDBC and ODBC drivers.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

Rich interactive visualizations to identify patterns in user and company flows with full visibility of computation. In less than 10 lines of code, a small set of sequence operators can provide full expressivity and finely-grained control. A query engine that allows you to trade between query speed, precision and cost according your needs. Motif currently uses a custom-built DSL, called Sequence Operations Language. We believe it is more natural than SQL and more powerful that a drag-and drop interface. We built a custom algorithm to optimize sequence queries. We also trade off precision for query speed, which is not used in decision-making.

PuppyGraph allows you to query multiple data stores in a single graph model. Graph databases can be expensive, require months of setup, and require a dedicated team. Traditional graph databases struggle to handle data beyond 100GB and can take hours to run queries with multiple hops. A separate graph database complicates architecture with fragile ETLs, and increases your total cost ownership (TCO). Connect to any data source, anywhere. Cross-cloud and cross region graph analytics. No ETLs are required, nor is data replication. PuppyGraph allows you to query data as a graph directly from your data lakes and warehouses. This eliminates the need for time-consuming ETL processes that are required with a traditional graph databases setup. No more data delays or failed ETL processes. PuppyGraph eliminates graph scaling issues by separating computation from storage.

Presto is an open-source distributed SQL query engine that allows interactive analytic queries against any data source, from gigabytes up to petabytes.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

Tabular is a table store that allows you to create an open table. It was created by the Apache Iceberg creators. Connect multiple computing frameworks and engines. Reduce query time and costs up to 50%. Centralize enforcement of RBAC policies. Connect any query engine, framework, or tool, including Athena BigQuery, Snowflake Databricks Trino Spark Python, Snowflake Redshift, Snowflake Databricks and Redshift. Smart compaction, data clustering and other automated services reduce storage costs by up to 50% and query times. Unify data access in the database or table. RBAC controls are easy to manage, enforce consistently, and audit. Centralize your security at the table. Tabular is easy-to-use and has RBAC, high-powered performance, and high ingestion under the hood. Tabular allows you to choose from multiple "best-of-breed" compute engines, based on their strengths. Assign privileges to the data warehouse database or table level.

Qubole is an open, secure, and simple Data Lake Platform that enables machine learning, streaming, or ad-hoc analysis. Our platform offers end-to-end services to reduce the time and effort needed to run Data pipelines and Streaming Analytics workloads on any cloud. Qubole is the only platform that offers more flexibility and openness for data workloads, while also lowering cloud data lake costs up to 50%. Qubole provides faster access to trusted, secure and reliable datasets of structured and unstructured data. This is useful for Machine Learning and Analytics. Users can efficiently perform ETL, analytics, or AI/ML workloads in an end-to-end fashion using best-of-breed engines, multiple formats and libraries, as well as languages that are adapted to data volume and variety, SLAs, and organizational policies.

Starburst allows you to make better decisions by having quick access to all of your data. Your company has more data than ever, but your data teams are still waiting to analyze it. Starburst gives your data teams quick and accurate access to more data. Starburst Enterprise, a fully supported, production-tested, enterprise-grade distribution for open source Trino (formerly Presto®, SQL), is now available. It increases performance and security, while making it easy for you to deploy, connect, manage, and manage your Trino environment. Starburst allows your team to connect to any source of data, whether it's on-premise, in a cloud, or across a hybrid cloud environment. This allows them to use the analytics tools they already love and access data that lives anywhere.

Polars, which is aware of the data-wrangling habits of its users, exposes a complete Python interface, including all of the features necessary to manipulate DataFrames. This includes an expression language, which will allow you to write readable, performant code. Polars was written in Rust to provide the Rust ecosystem with a feature-complete DataFrame interface. Use it as either a DataFrame Library or as a query backend for your Data Models.

Amazon Timestream is a fast, scalable and serverless time series data service for IoT/operational applications. It makes it possible to store and analyze trillions per day up to 1000 times faster than traditional relational databases and at as low as 1/10th of the cost. Amazon Timestream helps you save time and money when managing the lifecycles of time series data. It stores recent data in memory and moves historical data to a cost-optimized storage tier according to user defined policies. Amazon Timestream's purpose-built query tool allows you to access and analyze both recent and historic data simultaneously, without having to specify in the query whether the data is in the in-memory tier or the cost-optimized. Amazon Timestream's built-in time series analytics functions allow you to identify trends and patterns within your data in real-time.

Your cloud data platform. Access to any data you need with unlimited scalability. All your data is available to you, with the near-infinite performance and concurrency required by your organization. You can seamlessly share and consume shared data across your organization to collaborate and solve your most difficult business problems. You can increase productivity and reduce time to value by collaborating with data professionals to quickly deliver integrated data solutions from any location in your organization. Our technology partners and system integrators can help you deploy Snowflake to your success, no matter if you are moving data into Snowflake.

Amazon Athena allows you to easily analyze data in Amazon S3 with standard SQL. Athena is serverless so there is no infrastructure to maintain and you only pay for the queries you run. Athena is simple to use. Simply point to your data in Amazon S3 and define the schema. Then, you can query standard SQL. Most results are delivered in a matter of seconds. Athena makes it easy to prepare your data for analysis without the need for complicated ETL jobs. Anyone with SQL skills can quickly analyze large-scale data sets. Athena integrates with AWS Glue Data Catalog out-of-the box. This allows you to create a unified metadata repositorie across multiple services, crawl data sources and discover schemas. You can also populate your Catalog by adding new and modified partition and table definitions. Schema versioning is possible.

Perhaps you want to allow your clients to access their LIMS data via the internet. AIS labPortal makes it possible to do exactly that. Sample analyses can be emailed to customers, but not as paper copies. Clients can access their data using their unique login and security code from their computer. This is safer, faster, and more environmentally friendly than sending paper copies of sample analyses to customers. labPortal, a web-based portal, securely stores client's sample information and data in a cloud. Clients can access this data instantly from any computer, tablet, or smartphone. LabPortal's interface is an 'inbox' design that is simple and easy to use. It features an enhanced query engine, conditional highlight and Microsoft Excel export. The software includes an easy-to-use sample registration tool that allows users to preregister samples online. Transcribing data can be tedious and time-consuming.

Log management. Upgraded Honeycomb. Honeycomb is designed for modern developers to help them understand and improve their log management. You can quickly query system logs, metrics, and traces to find unknown unknowns. Interactive charts provide the most detailed view against raw, high-cardinality data. You can set Service Level Objectives (SLOs), based on what users are most interested in, to reduce noise alerts and prioritize work. Customers will be happy if you reduce on-call time, ship code faster, and minimize the amount of work required. Find the cause. Optimize your code. View your prod in high-res.

Debugging should not be complicated. AQTime Pro converts complex performance and memory information into easily digestible and actionable insights that allow you to quickly identify the root cause of bugs. Code bugs can drain your life! AQTime Pro makes it simple to find and squash highly differentiated bugs. You can quickly find memory leaks and performance bottlenecks as well as code coverage gaps with just a few clicks using over a dozen profilers. AQTime Pro allows you to find and fix all bugs in one tool, so you can get back to writing high-quality code. Code profilers shouldn't limit you to a single framework or codebase. They can prevent you from finding performance bottlenecks and memory leaks as well as code coverage gaps that are unique to your project. AQTime Pro can be used across multiple codebases or frameworks in a project. It supports many languages, including C/C++ and Delphi,.NET and Java.

Free & Open Source. Cross-platform. Supports all EVM Blockchains Solidity Debugger Pro is a VS Code Extension that adds rich debugging features for Solidity projects. It supports all EVM compatible blockchains and allows developers to easily debug Smart Contracts locally or on a node forked. Sdbg provides native debugging for the popular Hardhat Framework.

You can see video, network, and logs that caused crashes and bugs in live apps. There is no need to reproduce intermittent bugs. Bugsee keeps all your important data safe and sound. All the important data is available in your app. You now know exactly what caused the unexpected behavior. The video shows all user actions, communication with backend and system states that led to the problem. Get statistics about similar crashes. Trends broken down by device type and OS version. Get the exact filename, method, and line number of the crash. You can see the states of all other threads that were running at the time of the crash. You can see all HTTP and HTTPS requests, responses and headers - body and headers - from and towards your app. All console logs of your app synchronized with the video and network traffic can be viewed.

You can add logs, metrics, and traces to production or staging directly from your IDE/CLI, in real time and on-demand. Lightrun can help you increase productivity and ensure 100% code-level observability. Lightrun allows you to insert logs and metrics even when the service is in progress. You can debug monolith microservices like Kubernetes and Docker Swarm, ECS and Big Data workers, as well as serverless. Quickly add a logline, instrument a measurement, or place a snapshot that can be taken on-demand. There is no need to recreate the production environment or redeploy. Once instrumentation has been invoked, data is printed to your log analysis tool, your editor, or an APM of choice. To analyze code behavior and find bottlenecks or errors, you can stop the running process. You can easily add large numbers of logs and snapshots, counters or timers to your program. The system won't be stopped or broken. Spend less time debugging, and more time programming. Debugging is done without the need to restart, redeploying, or reproduce.

Xdebug is a PHP extension that provides a variety of features to enhance the PHP development experience. You can step through your code while the script is running in your editor or IDE. A new var_dump() function that allows you to stack trace your code to highlight warnings, errors and exceptions. Writes every function call, including arguments and invocation location to the disk. Optionally, includes each variable assignment and return value for each of the functions. With the help of visualization tools, you can analyze the performance of your PHP app and identify bottlenecks. This tool allows you to see which parts of your code are executed when PHPUnit runs unit tests. The fastest way to install Xdebug is often with a package manager. You can replace the PHP version you are using with the version that is compatible. Xdebug can be installed via PECL on Linux & macOS using Homebrew.

You can become a web performance and debugging guru by using the best in-browser HTTP sniffer. You can debug network traffic generated by a website directly from your browser, without needing to use a separate tool. You can accurately measure the network performance of a website and identify opportunities to improve it. Even with encrypted HTTPS traffic, no additional configuration or proxy is required. You can quickly identify weak SSL configurations or other security-related issues in your web server. Anyone can download the Basic Edition for free to receive full log files that will help you remotely diagnose problems or issues with performance. The HttpWatch API allows you to gather performance data from automated website tests. HttpWatch integrates Chrome, Edge, and Internet Explorer browsers to show the HTTP and HTTPS traffic generated when you access a website. HttpWatch allows you to select a request and all the information you need is displayed in a tabbed browser.

Unexpected outcomes happen all the time. You can pinpoint the source of errors or surprises in real-time with surgical precision when you have full visibility of the entire chain of calls. Unit testing is a key component of software engineering to create production-ready, performant applications. LangSmith offers the same functionality for LLM apps. LangSmith allows you to create test datasets, execute your applications on them, and view results without leaving the application. LangSmith allows mission-critical observability in just a few lines. LangSmith was designed to help developers harness LLMs' power and manage their complexity. We don't just build tools. We are establishing best practices that you can rely upon. Build and deploy LLM apps with confidence. Stats on application-level usage. Feedback collection. Filter traces and cost measurement. Dataset curation - compare chain performance - AI-assisted assessment & embrace best practices.

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 provides continuous profiling that is always on, zero-instrumentation, and helps to improve performance, reduce infrastructure costs, and understand incidents. You can optimize performance and save costs in your infrastructure with just one command. You can go back in time and pinpoint incidents or issues. Profiling data can provide unique insights and depth about how a process has performed over time. Use the profiling data gathered over time to confidently identify hot paths that can be optimized. Many organizations waste 20-30% of their resources on code paths that are easily optimized. Polar Signals Cloud is a unique blend of technologies that was designed to provide the profiling toolset necessary for today's changing infrastructure and applications. Deploy immediately with zero instrumentation and benefit from actionable observability information.

Tool for developers to monitor and improve AI powered by LLMs or other foundation models. Our simple SDK provides you with an intuitive and actionable overview of how your generative AI application is performing in production. Integrate LLM into your existing codebase, developer workflow and workflow. You can maintain complete control of your data by using our audit logs and fine-grained controls. Get actionable insights into how to improve LLM interactions. These teams are not only best equipped to integrate these capabilities into existing software, but also their proclivity for deployment, iteration, and improvement will be more relevant going forward. We believe that as software becomes more malleable, engineering teams will be the ones to turn this malleability into a delightful and hyper-personalized experience for users. The generative AI revolution will be led by developers.

You already know how to use Jam if you've ever taken a screen capture or recorded a video. Jam automatically creates bug reports that include all the information engineers need. Jam creates better reports, no matter what tool you use. Share links to your bug report or send them directly into your favorite issue tracker. You can still benefit from your teammates, whether or not they have signed up. Jam contains everything an engineer requires to debug. We have eliminated the need for follow-up calls in order to share additional context. All they need is there. Perfect bug reports Every time No more back-and-forth. Integrates tightly with all your favorite issue trackers and tools. It's as easy as taking a screen shot. Trusted by hundreds of thousands of teams in companies. Report bugs instantly without disrupting your workflow. Jam supports developers in the code review and test cycles by capturing diagnostic data, and replaying the bug instantly to identify what needs fixing.

QuasarDB is Quasar's brain. It is a high-performance distributed, column-oriented, timeseries database management software system that delivers real-time data for petascale use cases. You can save up to 20X on your disk usage Quasardb compression and ingestion are unmatched. Feature extraction can be performed up to 10,000 times faster. QuasarDB is able to extract features from raw data in real-time thanks to a combination of a builtin map/reduce engine, an aggregate engine that leverages SIMD from modern processors, and stochastic indices that consume virtually no disk space.

VeloDB, powered by Apache Doris is a modern database for real-time analytics at scale. In seconds, micro-batch data can be ingested using a push-based system. Storage engine with upserts, appends and pre-aggregations in real-time. Unmatched performance in real-time data service and interactive ad hoc queries. Not only structured data, but also semi-structured. Not only real-time analytics, but also batch processing. Not only run queries against internal data, but also work as an federated query engine to access external databases and data lakes. Distributed design to support linear scalability. Resource usage can be adjusted flexibly to meet workload requirements, whether on-premise or cloud deployment, separation or integration. Apache Doris is fully compatible and built on this open source software. Support MySQL functions, protocol, and SQL to allow easy integration with other tools.

ClickHouse is an open-source OLAP database management software that is fast and easy to use. It is column-oriented, and can generate real-time analytical reports by using SQL queries. ClickHouse's performance is superior to comparable column-oriented database management software currently on the market. It processes hundreds of millions of rows to more than a million and tens if not thousands of gigabytes per second. ClickHouse makes use of all hardware available to process every query as quickly as possible. Peak processing speed for a single query is more than 2 Terabytes per Second (after decompression, only utilized columns). To reduce latency, reads in distributed setups are automatically balanced between healthy replicas. ClickHouse supports multimaster asynchronous replication, and can be deployed across multiple datacenters. Each node is equal, which prevents single points of failure.

Palo helps enterprises create the PB level MPP architecture data warehouse services in just a few minutes and import massive data from RDS BOS and BMR. Palo is able to perform multi-dimensional analysis of big data. Palo is compatible to mainstream BI tools. Data analysts can quickly gain insights by analyzing and displaying the data visually. It has an industry-leading MPP engine with column storage, intelligent indexes, and vector execution functions. It can also provide advanced analytics, window functions and in-library analytics. You can create a materialized table and change its structure without suspending service. It supports flexible data recovery.

Now that your data has been in motion, it is time to make sense. Stream processing allows you to extract instant insights from your data streams but it can be difficult to set up the infrastructure. Confluent created ksqlDB to support stream processing applications. Continuously processing streams of data from your business will make your data actionable. The intuitive syntax of ksqlDB allows you to quickly access and augment Kafka data, allowing development teams to create innovative customer experiences and meet data-driven operational requirements. ksqlDB is a single solution that allows you to collect streams of data, enrich them and then serve queries on new derived streams or tables. This means that there is less infrastructure to manage, scale, secure, and deploy. You can now focus on the important things -- innovation -- with fewer moving parts in your data architecture.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

Apache Hive™, a data warehouse software, facilitates the reading, writing and management of large datasets that are stored in distributed storage using SQL. Structure can be projected onto existing data. Hive provides a command line tool and a JDBC driver to allow users to connect to it. Apache Hive is an Apache Software Foundation open-source project. It was previously a subproject to Apache® Hadoop®, but it has now become a top-level project. We encourage you to read about the project and share your knowledge. To execute traditional SQL queries, you must use the MapReduce Java API. Hive provides the SQL abstraction needed to integrate SQL-like query (HiveQL), into the underlying Java. This is in addition to the Java API that implements queries.

Impala offers low latency, high concurrency, and a wide range of storage options, including Iceberg and open data formats. Impala scales linearly in multitenant environments. Impala integrates native Hadoop security, Kerberos authentication, and the Ranger module to ensure that the correct users and applications have access to the right data. Utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment, with no redundant infrastructure or data conversion/duplication. Impala uses the same metadata driver and ODBC driver as Apache Hive. Impala, like Hive, supports SQL. You don't need to reinvent the wheel. Impala allows more users to interact with data, whether they are using SQL queries or BI apps, through a single repository. Metadata is also stored from the source of the data until it has been analyzed.