# Data Management Published articles for Data Management. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A Case Study: Building an EN 18031-Compliant IoT Solution with ESP32-C5 and ESP RainMaker DevFeed: [A Case Study: Building an EN 18031-Compliant IoT Solution with ESP32-C5 and ESP RainMaker]() Original publisher: [Read original article]() Author: John Lee Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32](), [Internet of things](), [Security](), [Cloud](), [Security & Privacy](), [Espressif](), [Authentication](), [Authorization]() Tags: [access-control](), [authentication](), [blog](), [compliance](), [connectivity](), [cryptographic](), [data](), [data-management](), [documentation](), [en-18031](), [esp-rainmaker](), [esp32-c5](), [espressif](), [eu](), [firmware](), [iot](), [privacy](), [rainmaker](), [regulatory](), [security](), [standards](), [storage]() ### AI overview This case study describes an ESP32-C5 and ESP RainMaker device-to-cloud IoT implementation assessed against EN 18031 cybersecurity and privacy requirements for products targeting the EU market. ### Source excerpt This case study assesses device-to-cloud IoT implementation of products based on ESP32-C5 and ESP RainMaker against the EN 18031 security requirements for the EU market. ## 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... ## Figma launches data residency in Japan DevFeed: [Figma launches data residency in Japan]() Original publisher: [Read original article]() Author: Figma Published: 2026-09-11T15:25:00Z Content type: release Language: en Sources: [Figma Blog]() Topics: [Figma](), [data]() Tags: [availability](), [collaboration](), [data](), [data-management](), [enterprise](), [figma](), [financial-services](), [healthcare](), [hosting](), [japan](), [launch](), [product-development](), [public-sector](), [security]() ### AI overview Figma launches data residency in Japan, allowing enterprise customers to host Figma file data domestically. The announcement highlights data protection, security, and data management needs across sectors including the public sector, healthcare, and financial services. ### Source excerpt Figma strengthens data protection and security for enterprise customers with local data hosting. ## TimesFM-3: A zero-shot foundation model for multivariate forecasting DevFeed: [TimesFM-3: A zero-shot foundation model for multivariate forecasting]() Original publisher: [Read original article]() Published: 2026-08-31T17:19:40Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [Time Series](), [Google](), [datasets](), [Transformer architecture](), [Fine-tuning](), [generalization in machine learning]() Tags: [data-management](), [features](), [fine-tuning](), [foundation-models](), [google](), [machine-intelligence](), [model](), [product](), [time-series](), [transformer-architecture](), [zero-shot]() ### AI overview Google Research introduces TimesFM-3, a 330-million-parameter time-series foundation model designed for accurate multivariate forecasting in a single forward pass. Pre-trained on more than one trillion real-world and synthetic time points, it jointly models coevolving series and external covariates in zero-shot settings without task-specific fine-tuning. ### Source excerpt Data Management ## The 4 Failure Modes of Agent Context in Production DevFeed: [The 4 Failure Modes of Agent Context in Production]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [data](), [data-architecture](), [Orchestration](), [real-time](), [systems]() Tags: [agent](), [agentic](), [agentic-ai](), [ai](), [ai-agent](), [amazon-simple-storage-service-s3](), [data-management](), [gartner](), [orchestration](), [real-time](), [tech-de]() ### AI overview The article examines four infrastructure failure modes that can undermine production AI agents: fragmented context, opacity, speed degradation, and non-accumulation. It explains how incomplete or stale information drawn from enterprise systems can produce confident but incorrect answers, and presents a real-time context layer as part of the solution. ### Source excerpt A production AI agent depends heavily on the context layer that tells it what to know at the moment it acts. It can pass every staging test, answer questions, call the right tools, and demo beautifully, then hit production and confidently offer a re... ## 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. ## Introducing TabFM: A zero-shot foundation model for tabular data DevFeed: [Introducing TabFM: A zero-shot foundation model for tabular data]() Original publisher: [Read original article]() Published: 2026-06-30T10:26:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [datasets](), [Machine learning](), [Google](), [Feature Engineering](), [Hyperparameter optimization](), [Language models](), [BigQuery](), [GitHub](), [hugging face](), [AI Inference]() Tags: [bigquery](), [classification](), [data](), [data-management](), [feature-engineering](), [github](), [google](), [hugging-face](), [hyperparameter-optimization](), [machine-intelligence](), [model](), [product](), [zero-shot]() ### AI overview Google Research introduces TabFM, a zero-shot foundation model for tabular-data classification and regression. It frames prediction as in-context learning, reducing the need for dataset-specific training, hyperparameter optimization, and feature engineering, with availability through Hugging Face, GitHub, and BigQuery. ### Source excerpt Data Management ## Optimizing cloud economics with linear elastic caching DevFeed: [Optimizing cloud economics with linear elastic caching]() Original publisher: [Read original article]() Published: 2026-06-25T10:03:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Caching](), [Cloud](), [Algorithms](), [Database](), [Serverless](), [Machine learning]() Tags: [algorithms](), [algorithms-theory](), [caching](), [cloud](), [cloud-services](), [cost](), [data-management](), [google](), [performance](), [real-time](), [serverless](), [systems]() ### AI overview Linear elastic caching applies the ski rental problem to dynamic cache sizing, balancing memory costs against cache misses. The approach treats memory as a time-dependent cost and adjusts cache capacity for changing workloads, aiming to reduce total cache-management expenses without compromising performance. ### Source excerpt Algorithms & Theory ## 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). ## 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. ## Meta's Infrastructure Evolution and the Advent of AI DevFeed: [Meta's Infrastructure Evolution and the Advent of AI]() Original publisher: [Read original article]() Author: Yee Jiun Song; Kaushik Veeraraghavan Published: 2025-09-29T13:00:15Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [Meta](), [Artificial Intelligence](), [Hardware](), [Network](), [cloud-infrastructure](), [Terraform]() Tags: [ai](), [ai-research](), [apache](), [data-center-engineering](), [data-centers](), [data-infrastructure](), [data-management](), [database](), [devinfra](), [hardware](), [infrastructure](), [lamp](), [linux](), [meta](), [ml-applications](), [mysql](), [network](), [networking-traffic](), [open-source](), [production-engineering](), [scale]() ### AI overview Meta describes how its infrastructure evolved from a small university-focused social network into a globally networked operation serving more than 3.4 billion people. The article explains that AI has changed infrastructure-scaling assumptions and requires innovation across hardware, software, networks, and data centers, while outlining earlier database, caching, social graph, ranking, and photo-service scaling work. ### Source excerpt Over the past 21 years, Meta has grown exponentially from a small social network connecting a few thousand people in a handful of universities in the U.S. into several apps and novel hardware products that serve over 3.4 billion people throughout the world. Our infrastructure has evolved significantly over the years, growing from a [...] Read More... The post Meta's Infrastructure Evolution and the Advent of AI appeared first on Engineering at Meta. ## 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 ## 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. ## Balancing Individual Contributor Work and Management in Data Leadership DevFeed: [Balancing Individual Contributor Work and Management in Data Leadership]() Original publisher: [Read original article]() Author: Doximity Published: 2024-07-22T15:00:00Z Content type: opinion Language: en Sources: [Doximity]() Topics: [Data Management](), [data-engineering](), [data analytics]() Tags: [code](), [data-analytics](), [data-engineering](), [data-management]() ### AI overview This opinion article examines the Data Strategist role and the player-coach model in data leadership. It argues that combining individual contributor work with people management can build trust, improve collaboration, and provide a closer understanding of technical challenges. ### Source excerpt The rise of a new title in the data industry, Data Strategist, caught my attention recently. Initially, I was skeptical. "Isn't this just a fancy term for a Data Manager?" I wondered. However, as I delved deeper, I realized my perspective was heavily influenced by my recent tenure at Doximity. At Doximity, the Data Engineering and Data Analytics Manager roles have always blended technical individual contributor work (often referred to as "IC Work") with responsibilities as people managers and functional leaders in the data organization and product. Prior to Doximity, I rarely had managers who had the expectations or bandwidth to do both. This player/coach role might sound too good to be true. Imagine a role where you could work with data, cultivate people and teams, and build data strategies that propel a business towards success, all without working 80 hours a week. After over five years as a manager at Doximity, I can confidently say it is not only possible, but it is also the best type of management role (and a role we are currently hiring for 🤩). Inspired, I reached out to our Data Leadership team here at Doximity to discuss why and how we aim to find balance amidst the chaos. Benefits of IC Work When Managing "Actions speak loudest. One of the fastest ways I found to gain trust from reports, peers, and stakeholders is by writing code to address a technical issue they care about. This approach works especially well if you are inheriting or joining a new team." - Doximity Data Analytics Manager "Understanding what all the teams are working on and enhancing collaboration among them is probably one of the best things about being a manager because you can facilitate communication to different silos that could benefit from each other." - Doximity Data Engineering Manager Whether it has been communicated explicitly or not, if you're expected to do IC work and management, you're doing two jobs. However, I view this as an opportunity to actively improve my skills and co ## Navigate your Turso database with Outerbase DevFeed: [Navigate your Turso database with Outerbase]() Original publisher: [Read original article]() Author: Brandon Strittmatter Published: 2024-06-26T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Data Management](), [SQLite](), [Data visualization](), [dashboards](), [SQL](), [Artificial Intelligence]() Tags: [ai](), [dashboards](), [data-management](), [data-visualization](), [database](), [sql](), [sqlite](), [turso]() ### AI overview This article explains how to connect a Turso database to Outerbase Studio and use it to view, edit, query, and visualize data. It describes table and schema editing, direct SQL queries, AI querying, dashboards, data visualization, and collaboration features. ### Source excerpt Query, edit, vizualise your Turso Database and more with Outerbase ## Edit records directly from the Neon console: meet the new Tables page DevFeed: [Edit records directly from the Neon console: meet the new Tables page]() Original publisher: [Read original article]() Author: Lachezar Petkov Published: 2024-06-14T15:53:39Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Drizzle](), [Data Management](), [Databases](), [SQL](), [CSV](), [JSON]() Tags: [data](), [data-management](), [database](), [drizzle](), [filter](), [json](), [product](), [sql](), [workflow]() ### AI overview Neon's new Tables page, powered by Drizzle Studio, lets users visually browse and manage database records from the Neon console. Users can add, update, and delete records, manage columns and tables, filter and paginate data, hide columns, and export data as JSON or CSV. ### Source excerpt A few weeks ago, we shipped a cool new feature in our console. In the past, the only way for Neon users to work with their data was via SQL queries; now, you can modify your data in an intuitive and visual way directly from the Tables page, powered by Drizzle Studio. You can now... [Next page]()