Search Results for "ms-sql" - Page 3
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11327 projects for "ms-sql" with 1 filter applied:
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
Cracking the Data Science Interview is an open educational repository that collects study materials, resources, and reference links for preparing for data science interviews. The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. The repository also provides links to recommended books, tutorials, practice platforms, and blog posts that help learners strengthen their theoretical and practical skills. In addition to conceptual study materials, the project includes interview question banks and case study prompts that simulate real hiring scenarios. ... -
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polynote
A better notebook for Scala (and more)
Polynote is an innovative polyglot notebook environment that improves on traditional interactive computing tools by enabling multi-language notebooks where users can mix languages like Scala, Python, SQL, and more within a single document while sharing variables and definitions seamlessly across those languages. Designed to address shortcomings in classic notebook solutions, it blends features familiar from IDEs — such as smart autocomplete, parameter hints, and better runtime insights — with the flexibility of notebook workflows, enhancing both developer productivity and data exploration. ... -
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Laravel Request Docs
Effortless API documentation tool for Laravel applications
The Hassle-Free automatic API documentation generation for Laravel. Automatic rules fetching from injected Request and by regexp. Automatic routes fetching from Laravel Routes. Support for Laravel logs. Support for SQL query and query time. Support for HTTP response time and memory consumption. Support for Authorization Headers. Support for File uploads, and support for Eloquents events. Display extra documentation using markdown. Saves the history of previous requests. Added filters to sort, group, and filter routes by methods, controllers, middleware, and routes. ... -
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AWS SDK for pandas
Easy integration with Athena, Glue, Redshift, Timestream, Neptune
...The library abstracts efficient patterns like partitioning, compression, and vectorized I/O so you get performant data lake operations without hand-rolling boilerplate. It also supports Redshift, OpenSearch, and other services, enabling ETL tasks that blend SQL engines and Python transformations. Operational helpers handle IAM, sessions, and concurrency while exposing knobs for encryption, versioning, and catalog consistency. The result is a productive workflow that keeps your analytics in Python while leveraging AWS-native storage and query engines at scale. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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Apache Drill
Apache Drill is a distributed MPP query layer for self describing data
...Leverage your existing SQL skillsets and BI tools including Tableau, Qlikview, MicroStrategy, Spotfire, Excel and more. Drill supports a variety of NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. A single query can join data from multiple datastores. -
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pg-aiguide
MCP server and Claude plugin for Postgres skills and documentation
pg-aiguide is a tool designed to enhance AI-assisted development with PostgreSQL by providing structured knowledge and skills directly to coding agents. It acts as a bridge between database documentation and AI tools, enabling more accurate generation of SQL queries and database interactions. The system integrates with Claude Code through an MCP server, allowing agents to access curated PostgreSQL knowledge in real time. It focuses on improving developer productivity by reducing errors and providing context-aware suggestions. The project also includes documentation and examples that help users understand best practices in database design and querying. ... -
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Data-Science-Interview-Questions-Answers
Curated list of data science interview questions and answers
...The repository focuses on core data science fundamentals rather than acting as a software framework, which makes it especially useful as a study and revision resource. Its content is organized into subject-specific documents that cover machine learning, deep learning, statistics, probability, Python, SQL and databases, and resume-based interview questions. That structure makes it practical for users who want to study by topic, strengthen weak areas, or simulate the range of questions they may encounter in interviews. -
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Data Science Interviews
Data science interview questions and answers
...Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. In addition to theoretical questions, the repository also includes practical interview topics related to coding challenges, SQL queries, and algorithmic thinking. -
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Localflare
local cloudflare
...Instead of juggling multiple CLIs and scattered logs, it gives you a unified UI for common Cloudflare primitives like D1 databases, KV namespaces, R2 buckets, Durable Objects, and Queues. The D1 experience is more than a viewer: it includes a SQL editor and a data browser with inline editing, filtering, sorting, bulk operations, and even dummy data generation so you can iterate quickly. For KV and R2 it provides practical file-and-record management, letting you view, update, delete, upload, and download as part of your normal dev loop. It also includes tools for testing queue behavior by sending messages and inspecting results, which helps validate event-driven logic without deploying. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Counter
Web Analytics made simple
...The backend is designed to be resource-light while still providing the essential metrics teams care about, such as page views, referrers, and basic geography. Rather than storing everything in a monolithic database, it combines a fast in-memory store for hot data with archival to a traditional SQL store for long-term retention. The client snippet is tiny and cookieless, which helps reduce layout shift and keeps page performance high. Because it’s a small Go service with a straightforward architecture, it can be self-hosted easily and integrated into existing stacks. The philosophy is to deliver “just enough” analytics for dashboards and reporting without the overhead or tracking footprint of big-box platforms. -
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jimmer
A revolutionary ORM framework for both java and kotlin.
...Brings dynamism into static language ORM frameworks, achieving flexible capabilities for operating on complex dynamic data structures without compromising the complete compile-time safety checks of static languages, eliminating DTO explosion and unafraid of engineering expansion and changing requirements. Its innovative SQL DSL supports dynamic table joins and native SQL embedding, gracefully handling real-world complexity. Focuses on business and reaping caching gains. CDC solutions decoupled from specific caching technologies are transparent to business code. Requires no special prior knowledge - veterans of any ORM can quickly and painlessly migrate. ... -
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Open Gauss
Project-scoped Lean workflow orchestrator from Math, Inc.
...It is based on the PostgreSQL ecosystem but significantly extends its capabilities through architectural optimizations, AI-driven features, and enterprise-level enhancements. The database organizes data using the relational model, storing structured information in tables composed of rows and columns while supporting standard SQL for querying and management. One of its defining strengths is its optimization for multi-core and distributed environments, allowing it to efficiently process high volumes of concurrent transactions with minimal latency. OpenGauss also incorporates AI-based optimization techniques, such as intelligent query planning, performance prediction, and automated tuning, which help reduce operational complexity and improve efficiency. -
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HeavyDB
HeavyDB (formerly MapD/OmniSciDB)
HeavyDB is an open-source GPU-accelerated analytical database designed to perform extremely fast queries on large datasets. The system is built as a SQL-based relational columnar database engine that leverages modern hardware parallelism, including GPUs and multicore CPUs. Its architecture allows users to query datasets containing billions of rows in milliseconds without requiring traditional indexing, pre-aggregation, or sampling techniques. HeavyDB was originally developed as part of the OmniSci platform (formerly MapD) and is commonly used for large-scale analytics and geospatial data processing. ... -
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Beam
A type-safe, non-TH Haskell SQL library and ORM
Beam is a Haskell interface to relational databases. Beam uses the Haskell type system to verify that queries are type-safe before sending them to the database server. Queries are written in a straightforward, natural monadic syntax. Combinators are provided for all standard SQL92 features, and a significant subset of SQL99, SQL2003, and SQL2008 features. Beam is standards-compliant but not naive. We recognize that different database backends provide different guarantees, syntaxes, and... -
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Apache Seata
High-performance, open source distributed transaction solution
...Its architecture separates responsibilities into a global coordinator and per-service participants, so business services remain decoupled while transactions are orchestrated centrally. Multiple modes are supported—AT (automatic, SQL-based with undo logs), TCC (try-confirm-cancel), Saga (long-running compensation), and XA—letting architects choose between simplicity, control, and interoperability. The AT mode is notable for minimizing code changes: Seata captures before/after images of SQL updates and can roll back on failure to maintain atomicity across services. ... -
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Kotlin Dataframe
Structured data processing in Kotlin
Data frame is an abstraction for working with structured data. Essentially it’s a 2-dimensional table with labeled columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dictionary of series objects. The handiness of this abstraction is not in the table itself but in a set of operations defined on it. The Kotlin Dataframe library is an idiomatic Kotlin DSL defining such operations. The process of working with data frame is often called data wrangling which is the process of transforming and mapping data from one "raw" data form into another format that is more appropriate for analytics and visualization. ... -
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MemMachine
Universal memory layer for AI Agents
...Unlike ephemeral LLM prompt state, MemMachine supports distinct memory types—short-term conversational context, long-term persistent knowledge, and profile memory for personalized facts—persisted in optimized stores (e.g., graph databases for episodic lines of reasoning and SQL for user facts) to support robust, context-aware intelligence in agents. It offers flexible APIs, a Python SDK, REST interfaces, and MCP (Model Context Protocol) connectivity to integrate seamlessly with agent frameworks receiving and storing memories over time, effectively boosting relevance, continuity, and tailored behavior. -
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Python 100 Days
Python - From Novice to Master in 100 Days
...The middle sections focus on real-world Python applications, including working with CSV, Excel, Word, PowerPoint, PDFs, images, email/SMS, and regular expressions. The curriculum expands into databases and SQL, Linux essentials, web fundamentals, and a substantial Practical Django track that covers ORM, sessions, RESTful APIs, caching with Redis, asynchronous tasks with Celery, authentication, testing, and deployment. Data analysis and visualization receive dedicated coverage via NumPy, pandas, matplotlib, seaborn, and pyecharts, followed by an applied machine learning track with kNN, trees, Bayes, regression, clustering, ensembles, and neural networks. -
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MetricFlow
MetricFlow allows you to define, build, and maintain metrics in code
...It works alongside a data stack—typically built with dbt—and allows you to express metrics as YAML‐based definitions tied to semantic models and dimension tables, rather than embedding logic ad-hoc across many dashboards or scripts. When a user or tool requests a metric (e.g., “monthly revenue by region”), MetricFlow generates optimized, warehouse-specific SQL to compute that metric, handling joins, filters, time grains, offsets, and other complexities under the hood. Because metric definitions live centrally, you avoid duplication across teams and tools, reduce risk of inconsistent numbers, and make it easier to audit and evolve the logic over time. The project emphasizes explainability, performance and portability: you define metrics once and then they can be consumed in BI tools, notebooks, or even AI/agent-driven workflows. -
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Neosync
Open Source Data Security Platform for Developers to Monitor
Neosync is a secure, open-source platform to generate, mask, and sync realistic test data across environments. It helps engineering teams create privacy-compliant datasets using synthetic data, transformations, and pseudonymization techniques. Designed with extensibility and data governance in mind, Neosync integrates with common databases and cloud services, enabling safe test environments for development and QA. -
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peepDB
CLI tool and python library to inspect databases fast
peepDB is an open-source command-line tool and Python library designed for developers and database administrators who need a fast and efficient way to inspect their database tables without writing SQL queries. With support for MySQL, PostgreSQL, and MariaDB, peepDB is lightweight, secure, and incredibly easy to use. -
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Sail
A drop-in Apache Spark replacement written in Rust
...It is built entirely in Rust, eliminating JVM overhead and enabling predictable performance, fast startup times, and improved memory safety compared to traditional big data frameworks. Sail is compatible with the Spark Connect protocol, which means existing Spark SQL and DataFrame workloads can run without code changes, making adoption seamless for teams already using Spark-based pipelines. The framework is designed to operate across a variety of environments, including local machines, Kubernetes clusters, and cloud deployments, allowing flexible scaling based on workload requirements. It also emphasizes cost efficiency, with benchmarks showing significant performance improvements and reduced infrastructure usage compared to traditional systems. -
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sqlite-utils
Python CLI utility and library for manipulating SQLite databases
...As a library, it exposes high-level APIs for inserting records, creating or transforming tables, normalizing schemas, and running migrations that SQLite’s limited ALTER TABLE cannot handle directly. The project also embraces an ecosystem of plugins, so you can add custom SQL functions, extra commands, or UIs (including a terminal UI) via separate packages. Because it’s designed by someone who uses SQLite heavily in real projects, the tool includes many small quality-of-life features. -
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Orpheus TTS
Towards Human-Sounding Speech
...The maintainers provide Colab notebooks, a standardized prompting format, and one-click deployment via Baseten for production-grade, FP8/FP16 optimized inference with ~200 ms streaming latency. -
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dplyr
dplyr: A grammar of data manipulation
dplyr is an R package that provides a consistent and intuitive grammar for data manipulation, enabling users to filter, arrange, summarize, and transform data efficiently. Part of the tidyverse ecosystem, dplyr simplifies complex data operations through a clear and readable syntax, whether working with data frames, tibbles, or databases. It is widely used in data science and statistical analysis workflows.
