# Data Management Data management is the development, execution, and supervision of practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more DevFeed: [Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more]() Original publisher: [Read original article]() Author: Bosmat Tuvel Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Redis](), [Data structures](), [Data Management](), [Open Source]() Tags: [data-management](), [improvements](), [memory](), [new-features](), [operations](), [performance](), [redis](), [streams](), [tech](), [time-series]() ### AI overview Redis 8.10 in Redis Open Source introduces compact hashes, incremental backup and restore, JSONPath syntax extensions, more flexible Stream consumption, new Set cardinality operations, atomic movement of multiple List elements, and enhanced Time Series capabilities. The release also improves memory efficiency, throughput, and operational reliability at scale. ### Source excerpt Redis 8.10 in Redis Open Source is now available, delivering improvements that make Redis more memory efficient, expressive, and easier to operate at scale. Highlights include compact hashes with up to 50% lower memory usage and 2x higher hash loadin... ## Martech stack audit: How to optimize your marketing toolset DevFeed: [Martech stack audit: How to optimize your marketing toolset]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-08-07T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [Optimization](), [Automation](), [Content Management System](), [Data Management](), [Search engine optimization (SEO)](), [Figma](), [webflow]() Tags: [analytics](), [audits](), [automation](), [content](), [cost](), [data](), [design](), [figma](), [how-to](), [inspiration](), [integration](), [learn](), [marketing](), [optimization](), [seo](), [software](), [tool](), [webflow](), [workflow](), [workflow-tools]() ### AI overview A practical guide to auditing a marketing technology stack, identifying inefficient or unnecessary tools, and finding opportunities to streamline workflows, improve collaboration, and increase return on investment. ### Source excerpt Learn when and how to conduct a martech stack audit that will identify opportunities to streamline operations, improve collaboration, and save resources. ## New reference architecture for an AI/ML Internal Developer Platform on GCP DevFeed: [New reference architecture for an AI/ML Internal Developer Platform on GCP]() Original publisher: [Read original article]() Author: Luca Galante Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [internal developer platform](), [Artificial Intelligence](), [Machine learning](), [Architecture & Design](), [Data Management](), [cloud-infrastructure](), [GPU](), [Inference](), [Monitoring](), [observability](), [Security]() Tags: [ai](), [ai-ml](), [architecture](), [data](), [gcp](), [gpu](), [inference](), [infrastructure](), [internal-developer-platform](), [monitoring](), [observability](), [security](), [technical]() ### AI overview The article presents a six-plane reference architecture for an AI/ML internal developer platform on GCP. It addresses the distinct needs of data-intensive and compute-heterogeneous workloads, including data management, dynamic CPU/GPU/TPU provisioning, model lifecycle management, security, and observability. ### Source excerpt Bridge the gap between AI pilots and production. Explore the new six-plane reference architecture for an AI/ML Internal Developer Platform (IDP) on GCP, designed to handle data-intensive and complex ML workloads. ## What's a digital experience platform and when is it right for your brand? DevFeed: [What's a digital experience platform and when is it right for your brand?]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-07-14T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [Data Management](), [Content Management System](), [data](), [Web](), [Cloud](), [Software]() Tags: [alternatives](), [business](), [content](), [customer](), [data](), [data-management](), [development](), [digital-experiences](), [management](), [marketing](), [web](), [workflows]() ### AI overview This guide explains digital experience platforms (DXPs), which connect website content with customer data, business systems, and workflows across the customer journey. It describes how DXPs differ from content management systems and when they can help organizations deliver more connected digital experiences. ### Source excerpt Discover what a digital experience platform (DXP) is, what you can use it for, and how it differs from systems like a CMS or CRM for business workflows. ## A 15-Second Health Check for Your Heroku Connect Data Pipeline DevFeed: [A 15-Second Health Check for Your Heroku Connect Data Pipeline]() Original publisher: [Read original article]() Author: Nick Prey Published: 2026-06-24T15:35:14Z Content type: tutorial Language: en Sources: [Heroku]() Topics: [Heroku](), [Command-line interface](), [Data Management](), [SQL](), [Databases]() Tags: [cli](), [data-pipeline](), [engineering](), [heroku](), [heroku-connect](), [migrations](), [salesforce](), [schema](), [synchronization](), [troubleshooting]() ### AI overview This tutorial explains how the Heroku Connect CLI plugin's diagnostic command checks connection health, schema alignment, and field configurations in a Salesforce-to-Heroku Postgres data pipeline. It highlights how the command can expose configuration mismatches and provide actionable warnings before schema updates or migrations. ### Source excerpt Heroku Connect is a fully managed, bidirectional data sync service between Salesforce and Heroku Postgres that lets developers read and write Salesforce data using standard SQL. The sync is straightforward to set up and operates smoothly in the background, but when a field does not update as expected or data seems to lag, you do not need to guess what went wrong. The post A 15-Second Health Check for Your Heroku Connect Data Pipeline appeared first on Heroku. ## Unlocking dependable responses with Gemini Enterprise Agent Platform's Agentic RAG DevFeed: [Unlocking dependable responses with Gemini Enterprise Agent Platform's Agentic RAG]() Original publisher: [Read original article]() Published: 2026-06-05T11:26:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Retrieval Augmented Generation (RAG)](), [Artificial Intelligence](), [Google](), [Cloud](), [Data Management](), [Large Language Model]() Tags: [agentic](), [ai](), [data-management](), [gemini](), [google](), [google-cloud](), [machine-intelligence](), [natural-language-processing](), [product](), [rag](), [reasoning](), [retrieval]() ### AI overview Google Research and Google Cloud introduce an agentic RAG framework for complex enterprise queries. Its multi-agent workflow plans and reasons across multiple data sources, iteratively searches for sufficient context, and aims to produce more dependable and accurate responses than single-step RAG. ### Source excerpt Data Management ## Integrating the Rust Delta Kernel into ClickHouse DevFeed: [Integrating the Rust Delta Kernel into ClickHouse]() Original publisher: [Read original article]() Author: Melvyn Peignon; Kseniia Sumarokova; Raúl Marín Published: 2026-05-22T09:32:20Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Rust](), [interoperability](), [schema-evolution](), [Data Management]() Tags: [clickhouse](), [data-management](), [guides](), [interoperability](), [performance](), [rust](), [schema-evolution](), [sql]() ### AI overview This article explains how ClickHouse integrated the Rust Delta Kernel to replace its native Delta Lake implementation. The integration provides a maintained interface for working with the table format, reduces integration and maintenance complexity, and enables features including writes, schema evolution, time travel, and partition pruning. ### Source excerpt How we integrated the Rust Delta Kernel to replace our native Delta Lake implementation, reducing maintenance overhead while unlocking writes, schema evolution, time travel, and partition pruning. ## Difference Between DBMS and RDBMS DevFeed: [Difference Between DBMS and RDBMS]() Original publisher: [Read original article]() Author: Sumit Malviya Published: 2026-03-13T07:49:01Z Content type: tutorial Language: en Sources: [Backend Archives | Talent500 blog]() Topics: [Databases](), [Database](), [Data Management]() Tags: [access-control](), [advantages-of-rdbms-over-dbms](), [backend](), [conclusion-choosing-between-dbms-and-rdbms](), [concurrency](), [core-concepts-of-rdbms](), [data-management](), [database](), [databases](), [dbms-vs-rdbms-key-differences](), [difference-between-rdbms-and-dbms](), [examples-of-dbms](), [future-trends-in-database-management](), [key-features-of-dbms](), [limitations-of-dbms-and-rdbms](), [popular-rdbms-examples](), [rdbms](), [security](), [software-engineering](), [summary-table-dbms-vs-rdbms](), [understanding-rdbms](), [what-is-dbms](), [when-to-use-dbms-vs-rdbms]() ### AI overview This tutorial explains the differences between database management systems (DBMS) and relational database management systems (RDBMS), including how DBMS software stores, retrieves, and manages data and how it supports integrity, security, access control, concurrency, and transactions. ### Source excerpt The data-driven world demands more than just managing information efficiently. It is critical for businesses, institutions, and applications that they [...] The post Difference Between DBMS and RDBMS appeared first on Talent500 blog. ## AI doesn't always generate perfect ClickHouse schemas (yet) DevFeed: [AI doesn't always generate perfect ClickHouse schemas (yet)]() Original publisher: [Read original article]() Author: Al Brown Published: 2026-03-13T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Artificial Intelligence](), [Agent Skills](), [Data Management](), [Optimization](), [Compression]() Tags: [agent-skills](), [ai](), [clickhouse](), [compression](), [data-management](), [optimization]() ### AI overview This article examines common mistakes in AI-generated ClickHouse schemas, including unnecessary partitioning, per-column codecs, and projections. It recommends starting with a simple schema, measuring real workloads, and adding complexity only when the data justifies it. ### Source excerpt This post walks through the common pitfalls we see when AI generates ClickHouse schemas, drawn from real conversations with our Solutions Architecture team and patterns across dozens of customer engagements. ## Using Materialized Views and Derived Datasets to Optimize Data Queries DevFeed: [Using Materialized Views and Derived Datasets to Optimize Data Queries]() Original publisher: [Read original article]() Published: 2025-12-07T09:05:00Z Content type: article Language: en Sources: [Gunnar Morling]() Topics: [data-architecture](), [Data Management](), [Database](), [datasets](), [Latency](), [elasticsearch](), [parquet]() Tags: [data](), [data-lake](), [data-management](), [database](), [datasets](), [elasticsearch](), [latency](), [parquet](), [performance]() ### AI overview The article explains how pull-based queries retrieve matching records at query time and why this can create performance, data-format, data-shape, and data-location challenges. It presents materialized views and derived datasets as a way to precompute query results and store them in an optimized format, shape, and location. ### Source excerpt Table of Contents Materialized Views Embracing Data Duplication Streams for machines, tables for humans Historically, data management systems have been built around the notion of pull queries: users query data which, for instance, is stored in tables in an RDBMS, Parquet files in a data lake, or a full-text index in Elasticsearch. When a user issues a query, the engine will produce the result set at that point in time by churning through the data set and finding all matching records (oftentimes sped up by utilizing indexes). ## Treating Data as Code at Two Sigma DevFeed: [Treating Data as Code at Two Sigma]() Original publisher: [Read original article]() Author: Emily Majewski Published: 2025-11-13T16:09:02Z Content type: article Language: en Sources: [Two Sigma Engineering]() Topics: [data-engineering](), [Data Management](), [Code](), [CI/CD](), [Terraform](), [Testing]() Tags: [ci-cd](), [code](), [data](), [data-engineering](), [deployment](), [infrastructure-as-code](), [quality]() ### AI overview This article explains Two Sigma's approach to treating data as code. It describes applying software development practices such as version control, automated testing, reproducibility, infrastructure as code, and CI/CD to data management, including the use of Terraform and dbt. ### Source excerpt The post Treating Data as Code at Two Sigma appeared first on Two Sigma. ## ClickHouse Open House Roadshow NYC videos are here DevFeed: [ClickHouse Open House Roadshow NYC videos are here]() Original publisher: [Read original article]() Author: Tanya Bragin Published: 2025-10-21T12:11:40Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [observability](), [Data Management](), [ai and ml](), [Data Science](), [Scalability]() Tags: [analytics](), [clickhouse](), [data-management](), [efficiency](), [infrastructure](), [observability](), [real-time](), [scalability](), [speed]() ### AI overview The ClickHouse Open House Roadshow in New York released recordings featuring customer and product-team presentations. The sessions cover real-time analytics, observability, interactive data warehousing, and AI and ML infrastructure, with examples from Capital One and Ramp. The article highlights improvements in response times, scalability, and infrastructure costs from using ClickHouse. ### Source excerpt The ClickHouse Open House Roadshow kicked off in New York on October 7th with compelling customer stories from Modal, Ramp, and Capital One, and all session videos are now live. ## Announcing cost-efficient storage with usage-based backups, cold storage, and Network file storage DevFeed: [Announcing cost-efficient storage with usage-based backups, cold storage, and Network file storage]() Original publisher: [Read original article]() Author: Nihar Namjoshi Published: 2025-10-02T08:05:09Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Filesystems](), [data](), [Data Management](), [cloud-infrastructure](), [datacenter]() Tags: [backups](), [cost](), [data](), [data-centers](), [digitalocean](), [network](), [product-updates](), [protection](), [space-object-storage](), [spaces](), [storage]() ### AI overview DigitalOcean announces generally available Network File Storage, usage-based backups, and Spaces cold storage. The release targets shared storage for AI and cloud workloads, infrequently accessed data, and stronger data protection policies. ### Source excerpt As data footprints grow, businesses need cost-efficient storage for infrequently accessed data, high-performance file systems for collaborative work, and more aggressive data protection policies to meet strict recovery objectives. We're introducing several significant enhancements to our storage portfolio to help you manage the challenges of data management, protection, and scaling. TL;DR Network file storage solution for high-performance AI workloads and cloud applications, is now generally available. You can access it in the DigitalOcean console. To learn more visit the product documentation page. Usage-based backups are now generally available to meet aggressive rpos. Check out our documentation to learn more and head over to the DigitalOcean console to enable backups for your Droplets or GPUs. Spaces cold storage for infrequently accessed data is now generally available. Visit our documentation to learn more and and head over to the DigitalOcean console to set up Spaces cold storage. Network file storage (NFS) for high-performance AI workloads Data-intensive applications, particularly in AI and machine learning, require shared, high-performance file storage that is easy to provision and manage. Our Network file storage service is now generally available in our ATL1 , NYC2 and AMS3 data centers. We have also introduced a new Standard Tier designed for general-purpose cloud applications like App Platform workloads, web applications, CMS platforms, general compute tasks, and legacy applications requiring shared file systems. This tier provides predictable, cost-effective shared storage with ReadWriteMany semantics for cloud applications. Customers with high-throughput or data-intensive needs, such as AI/ML, GPU training, high-throughput analytics, or those requiring parallel multi-node access, should instead utilize the NFS High Performance Tier, which supports superior throughput scaling for multi-node environments and demanding data pipelines. NFS supports share ## KVS: The key-value storage engine that powers the Fury ecosystem DevFeed: [KVS: The key-value storage engine that powers the Fury ecosystem]() Original publisher: [Read original article]() Author: Ariel Zach Published: 2025-10-01T14:09:08Z Content type: article Language: en Sources: [Mercado Libre Tech]() Topics: [NoSQL](), [Persistence](), [Scalability](), [Low Latency](), [Availability](), [Data Management]() Tags: [architecture](), [availability](), [data](), [database](), [databases](), [development](), [distributed](), [fury](), [infrastructure](), [key-value-store](), [kvs](), [low-latency](), [nosql](), [persistence](), [resilience](), [scalability](), [security](), [storage]() ### AI overview This article explains KVS, a key-value storage system in the Fury ecosystem. It describes how unique key-value pairs enable fast access, and reports that KVS supports large-scale distributed persistence with automatic scaling, low latency, availability, and recovery mechanisms. ### Source excerpt In modern software development, the efficiency, scalability, and resilience of data storage systems are critical to any platform's success. In this context, KVS (Key-Value Store) emerges as a fundamental component within the Fury ecosystem, offering a robust and flexible solution for data management. This article explores what KVS is, how it works, and why it's so important for Fury and its users. What is KVS? KVS, or Key-Value Store, is a data storage system based on the key-value paradigm. Unlike traditional relational databases, which organize data in tables and rows, KVS stores each data element as a unique key-value pair. This approach simplifies access and manipulation of information, enabling extremely fast and efficient operations. In Fury, KVS isn't just another database -- it's the fundamental engine that drives distributed, scalable, and fault-tolerant data persistence and retrieval. Currently, KVS processes over 642 million operations per minute, including 572 million reads and 70 million writes. The underlying infrastructure scales automatically and maintains low latency even under high-demand scenarios. The service is used by more than 7,600 applications within Mercado Libre (around 25% of the total), making it a key component of the ecosystem. Overall, it stores over 9 petabytes of data with mechanisms that ensure availability and recovery in case of failures. How does KVS work? Each piece of data is stored under a unique key. To retrieve or modify a value, you only need to know its key, eliminating the need for complex queries and relationships. This speeds up both read and write operations. KVS is built on top of the Fury ecosystem, which provides key guarantees such as scalability, security, and traffic control. It uses an architecture based on a fully managed NoSQL solution, designed to automatically adapt to demand. Thanks to this infrastructure, KVS inherits advanced scalability capabilities, allowing it to grow automatically and transparently, c ## Build a Personalized AI Assistant with Postgres DevFeed: [Build a Personalized AI Assistant with Postgres]() Original publisher: [Read original article]() Author: Saxon Fletcher Published: 2025-06-25T07:00:00Z Content type: tutorial Language: en Sources: [Supabase Blog]() Topics: [Artificial Intelligence](), [PostgreSQL](), [Supabase](), [Large Language Model](), [Data Management](), [Databases](), [Embeddings](), [Model Context Protocol](), [AI search](), [Security]() Tags: [ai](), [data](), [embeddings](), [external](), [how-to](), [large-language-models](), [mcp](), [memory](), [postgres](), [tools]() ### AI overview This tutorial explains how to build a personalized AI assistant with Supabase and PostgreSQL. The design combines LLM-managed structured data, scoped database permissions, message history, vector-based semantic memory, structured SQL memory, scheduled prompts, web search, and MCP integrations for long-term memory and autonomous actions. ### Source excerpt Learn how to build a Supabase powered AI assistant that combines PostgreSQL with scheduling and external tools for long-term memory, structured data management and autonomous actions. ## Scaling our Observability platform beyond 100 Petabytes by embracing wide events and replacing OTel DevFeed: [Scaling our Observability platform beyond 100 Petabytes by embracing wide events and replacing OTel]() Original publisher: [Read original article]() Author: Rory Crispin; Dale McDiarmid Published: 2025-06-19T13:55:13Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [log management](), [OpenTelemetry](), [Data Management](), [Grafana]() Tags: [clickhouse](), [cost](), [efficiency](), [logging](), [logs](), [observability](), [opentelemetry](), [pipeline]() ### AI overview The article describes how ClickHouse scaled its internal observability platform from 19 PiB to more than 100 petabytes and nearly 500 trillion rows. It explains why OpenTelemetry parsing and marshalling became a bottleneck, how a native ClickHouse-to-ClickHouse pipeline and wide events enabled a 20x increase in event volume while using less than 10% of the previous CPU, and how HyperDX and ClickStack changed the observability user experience. ### Source excerpt Read how we scaled our observability platform from 19PB to 100PB and 500 trillion rows by replacing OpenTelemetry with a native ClickHouse-to-ClickHouse pipeline, embracing wide events and cutting CPU usage by 90%. ## Getting Started with GenAI Using CockroachDB DevFeed: [Getting Started with GenAI Using CockroachDB]() Original publisher: [Read original article]() Author: Amine El Kouhen, Ph.D. Published: 2025-06-05T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Embeddings](), [genai](), [Generative AI](), [AI search](), [Data Management](), [Databases](), [data]() Tags: [ai](), [cockroachdb](), [data](), [data-management](), [databases](), [embeddings](), [genai](), [generative-ai](), [retrieval](), [vector-database]() ### AI overview This introductory article explains how generative AI relates to vector embeddings and vector databases, and previews how CockroachDB can support vector search, data consistency, search, classification, and recommendations. ### Source excerpt Information today is generated and consumed in unprecedented magnitudes. With every click, swipe, and transaction, massive amounts of data are collected, waiting to be harnessed for insights, decision-making, and innovation. Today, more than 80% of the data that organizations generate is unstructured - and the amount of this data type will only grow in the coming decades. ## Developing an Internal Tool for Our Puzzle Editor DevFeed: [Developing an Internal Tool for Our Puzzle Editor]() Original publisher: [Read original article]() Author: The NYT Open Team Published: 2025-06-02T15:54:44Z Content type: article Language: en Sources: [New York Times]() Topics: [dashboards](), [Data Management](), [interface](), [payload](), [Testing](), [Tool]() Tags: [code](), [dashboard](), [data-management](), [developing](), [interface](), [internal-tools](), [payload](), [puzzle](), [testing](), [tool](), [web-development](), [workflow]() ### AI overview The article describes how The New York Times developed the Connections Reference Dashboard, an internal tool for managing puzzle data and supporting the Connections editor's workflow. It explains the tool's interface, handling of changing puzzle data, search capabilities, and reduction of manual cross-referencing steps. ### Source excerpt How we developed a dashboard tool created to help ease the workflow of managing puzzles for our Connections editor.Illustration by Su Yun Song By Shafik Quoraishee and Wyna Liu In the game Connections, every puzzle is a meticulously crafted challenge designed to captivate our audience and spark intellectual curiosity. Developing these puzzles can sometimes be a time consuming and intricate task. Each puzzle requires planning, beginning with conceptualizing fresh categories and plausible misleads, followed by testing the combinations for balance and solvability, and concluding with refinement and publication-ready formatting. The process requires both creativity and quality control. Wyna Liu, the editor of Connections has the responsibility of constructing and reviewing multiple puzzles spanning various dates, ensuring that each board remains consistent, fresh and challenging to our puzzle solvers. This is a challenging endeavor where there isn't much room for error. In order to address the challenge, we developed the Connections Reference Dashboard -- an in company tool aimed at streamlining data management while providing the puzzle editor with an intuitive, aesthetically pleasing interface that enhances the daily workflow. There were two considerations in developing this tool. Firstly was technical work in handling a dynamically changing payload of puzzle data. We wanted to create a rich and visually resonating interface that was easy to navigate and gave a bit of the feel of the Connections game itself. Therefore, everything from the board results to the search interface was designed with these ergonomics in mind. We wanted to create a level of tactility to the tool which was reminiscent of and which reduced the number of manual steps needed to cross reference both categories and words in individual boards. The primary functionality that Wyna was after was the ability to quickly identify words that have appeared in previous Connections boards, as well as their con ## Trading Airflow + EMR for Temporal + Bauplan: The Mediaset tale DevFeed: [Trading Airflow + EMR for Temporal + Bauplan: The Mediaset tale]() Original publisher: [Read original article]() Author: Stu Kendall Published: 2025-04-10T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [airflow](), [Orchestration](), [Data Management](), [Amazon Web Services](), [Python](), [Amazon S3](), [data](), [Apache Iceberg]() Tags: [airflow](), [aws](), [community](), [data-management](), [formats](), [orchestration](), [python](), [reliability](), [s3]() ### AI overview This article describes how Mediaset replaced an Airflow-based AWS data stack with Temporal and Bauplan. The change produced a near-real-time news dashboard, with refresh time reduced from one hour to five minutes according to the supplied summary. ### Source excerpt Mediaset replaced Airflow and six AWS services with Temporal and Bauplan, cutting dashboard refresh time from 1 hour to 5 minutes in just 6 weeks. ## Analytics for Per-User Database Architecture DevFeed: [Analytics for Per-User Database Architecture]() Original publisher: [Read original article]() Author: Jamie Barton Published: 2024-11-18T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Per-user Database](), [Multitenancy](), [Turso](), [SQLite](), [Platform API](), [Data Management](), [Architecture & Design](), [Software as a service](), [data-architecture](), [Databases]() Tags: [architecture](), [data-management](), [database](), [databases](), [etl](), [metrics](), [monitor](), [per-user-database](), [platform-api](), [reporting](), [saas](), [sqlite](), [turso]() ### AI overview This article explains how to aggregate analytics from a per-user or per-tenant Turso database architecture. It describes using an ETL script to query individual SQLite databases, collect metrics such as orders, revenue, shopping carts, and products, and store the results in a central database for reporting and platform monitoring. ### Source excerpt Aggregating Multi-Tenant Databases for Analytics and Reporting ## Multi-function USB Dongle based on ESP32-S3 DevFeed: [Multi-function USB Dongle based on ESP32-S3]() Original publisher: [Read original article]() Author: John Lee Published: 2024-10-24T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32-S3](), [USB](), [Data Management]() Tags: [blog](), [dongle](), [esp32-s3](), [espressif](), [http-server](), [usb]() ### AI overview The article presents a multi-function USB dongle based on the Espressif ESP32-S3 module. It addresses cumbersome workflows for transferring camera photos, printing files, and sharing videos, and describes a device combining a USB MSC wireless disk with a USB wireless network card and dual-function switching. ### Source excerpt In modern digital life, we often encounter scenarios where data transfer is not flexible or efficient enough, especially when exporting camera photos, printing files, and sharing videos. In this article, we will have a closer look at the mentioned scenarios and propose a solution - Multi-function USB Dongle based on ESP32-S3. ## MongoDB Realm & Device Sync Alternatives - Supabase DevFeed: [MongoDB Realm & Device Sync Alternatives - Supabase]() Original publisher: [Read original article]() Author: Craig Cannon Published: 2024-10-09T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [Local-First](), [real-time](), [MongoDB](), [Low Latency](), [Data Management](), [Database](), [React Native]() Tags: [alternatives](), [announcement](), [integration](), [local-first](), [mongodb](), [offline](), [outages](), [partner](), [performance](), [react-native](), [real-time](), [sync]() ### AI overview Supabase presents alternatives to MongoDB Realm and Device Sync for building offline-first, real-time applications. The article highlights Legend-State, WatermelonDB, PowerSync, Replicache, and ElectricSQL, with capabilities including synchronization, conflict handling, offline operation, and multi-user collaboration. ### Source excerpt Learn how Supabase can help you transition from MongoDB Realm and Device Sync. ## SQL basecamps before Trino Summit DevFeed: [SQL basecamps before Trino Summit]() Original publisher: [Read original article]() Author: Manfred Moser Published: 2024-10-07T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [SQL](), [Data Management](), [Filesystems](), [configuration]() Tags: [configuration](), [data-management](), [object-storage](), [operational](), [series](), [sql](), [summit](), [tips-and-tricks](), [training]() ### AI overview The article introduces two SQL training sessions designed to prepare Trino users for Trino Summit 2024. The sessions cover moving data into and with Trino, lakehouse migration, schemas and tables, data management, object-storage configuration, file-system support, querying, window functions, and complex structural data. ### Source excerpt Later in December your knowledge of our Trino SQL query engine will certainly peak again at Trino Summit 2024. To reach those heights and absorb all there is to learn at Trino Summit, you need to get ready. That is why I teamed up with our Trino creators and BDFLs - Martin Traverso, Dain Sundstrom, and David Phillips. We aim to be your coaches and trainers to get you ready and get to the summit without the need for oxygen masks and sherpas. Join us for the "SQL basecamps before Trino Summit", where we expand on our past SQL training series with two new episodes. Register now ## What is Data Residency? Data Residency Concerns for Global Applications DevFeed: [What is Data Residency? Data Residency Concerns for Global Applications]() Original publisher: [Read original article]() Author: Ethan Limchayseng Published: 2024-08-22T21:58:11Z Content type: article Language: en Sources: [Heroku]() Topics: [data](), [Data Management](), [Cloud](), [Security, Privacy and Abuse Prevention](), [Heroku]() Tags: [cloud](), [cloud-infrastructure](), [compliance](), [data](), [data-management](), [data-protection](), [dynos](), [engineering](), [heroku](), [privacy](), [private-spaces](), [security-compliance]() ### AI overview This article explains data residency, including the legal requirements governing where data is stored and processed. It discusses how global applications affect compliance obligations and notes that cloud regions and services such as Private Dynos from Heroku Enterprise may help address those requirements. ### Source excerpt Data Residency Compliance Is Possible with the Right Cloud Provider Because today's companies operate in the cloud, they can reach a global audience with ease. At any given moment, you could have customers from Indiana, Indonesia, and Ireland using your services or purchasing your products. With such a widespread customer base, your business data will [...] The post What is Data Residency? Data Residency Concerns for Global Applications appeared first on Heroku. [Next page]()