# data analytics Data analytics converts raw data into actionable insights using tools, technologies, and processes to find trends and solve problems. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The Art of Simplifying Decisions. DevFeed: [The Art of Simplifying Decisions.]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-08-20T12:27:37Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [decision-making](), [data analytics](), [Artificial Intelligence]() Tags: [ai-agents](), [analysis](), [data-analytics](), [decision-making]() ### AI overview The article argues that decision-making has become a bottleneck as coding becomes more scalable. It compares AI-supported options, additional meetings, and decision-making within limited scope, while emphasizing the need for context, evidence, and empowered product decisions. ### Source excerpt Decision-making is the bottleneck today. ## The CI/CD moment for data analytics DevFeed: [The CI/CD moment for data analytics]() Original publisher: [Read original article]() Author: Gaurav Nanda Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [data analytics](), [DataOps](), [real-time](), [data-engineering](), [CI/CD](), [Kafka](), [apache-flink]() Tags: [ai](), [analytics](), [apache-flink](), [architectures](), [bridging](), [cost](), [data-analytics](), [data-engineering](), [engineering](), [event](), [fraud](), [google](), [insights](), [kafka](), [latency](), [real-time](), [scale](), [streaming](), [systems](), [tools]() ### AI overview The article argues that data analytics is approaching a CI/CD-like transition toward continuous analytics. It describes how multi-step pipelines introduce latency, operational risk, and maintenance burden, and explains why real-time insight is becoming a baseline platform capability for applications such as personalization, fraud detection, reliability, and AI-driven features. ### Source excerpt How converging OLTP and OLAP architectures are driving 'Continuous Analytics', the CI/CD moment for data to deliver real-time, unified insights and operational simplicity. ## How Visa went from multi-day reporting to conversational analytics agents with ClickHouse Cloud and LibreChat DevFeed: [How Visa went from multi-day reporting to conversational analytics agents with ClickHouse Cloud and LibreChat]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-24T15:12:01Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data analytics](), [Conversational AI](), [MCP Server](), [Model Context Protocol (MCP)](), [Amazon Bedrock](), [Cloud](), [Amazon Web Services](), [Security](), [SQL](), [Open Source]() Tags: [ai](), [analytics](), [apis](), [aws](), [clickhouse](), [cloud](), [conversational-ai](), [data-analytics](), [mcp](), [mcp-server](), [open-source](), [security](), [sql]() ### AI overview Visa used ClickHouse Cloud on AWS, LibreChat, and the ClickHouse MCP server to build conversational BI agents for Authorize.net payments data. The system enables natural-language analysis across roughly 40 petabytes and reduced multi-day reporting to sub-second answers, while materialized views improved analysis speed and reclaimed 8-10 hours per user each week. ### Source excerpt Visa paired LibreChat with ClickHouse Cloud to build conversational BI agents on Authorize.net payments data, cutting multi-day reports to sub-second queries and reclaiming 8-10 hours per user each week. ## Codex for every role, tool, and workflow DevFeed: [Codex for every role, tool, and workflow]() Original publisher: [Read original article]() Published: 2026-06-02T09:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [codex](), [Artificial Intelligence](), [software-development](), [data analytics](), [dashboards](), [Incident response](), [Machine learning]() Tags: [ai](), [codex](), [dashboards](), [data-analytics](), [developers](), [incident-response](), [machine-learning](), [product](), [software-development](), [workflow](), [workflows]() ### AI overview OpenAI introduces role-specific Codex plugins, annotations, and a preview of shareable interactive websites and apps. The plugins connect Codex to existing tools and workflows for analytics, creative production, sales, and other knowledge-work roles, including data exploration, dashboards, campaign assets, customer-context workflows, and machine-learning infrastructure experiments. ### Source excerpt Discover new Codex plugins, sites, and annotations that help analysts, marketers, designers, investors, and other teams get more done with AI. ## Route specialized workloads DevFeed: [Route specialized workloads]() Original publisher: [Read original article]() Author: Cecil Phillip Published: 2026-05-11T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [GPU](), [Machine learning](), [cpu](), [data analytics](), [Hardware]() Tags: [analytics](), [cost](), [cpu](), [gpu](), [ml](), [queues](), [scaling](), [separate](), [temporal-concepts](), [worker]() ### AI overview The article describes a Temporal Validated Pattern for routing Activities with different resource requirements to separate Task Queues. Dedicated Worker pools can handle GPU-intensive machine learning, CPU, high-memory analytics, and specialized hardware workloads independently. ### Source excerpt Route ML, analytics, and CPU Activities to separate Temporal Task Queues. Cut GPU costs by 60-80% while scaling each Worker pool independently. ## Gala supercharges analytics performance with ClickHouse on AWS DevFeed: [Gala supercharges analytics performance with ClickHouse on AWS]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-05-04T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Amazon Web Services (AWS)](), [data analytics](), [Data Infrastructure](), [data-platforms](), [telemetry](), [databricks](), [Blockchain]() Tags: [aws](), [blockchain](), [clickhouse](), [data-analytics](), [data-infrastructure](), [data-platforms](), [databricks](), [telemetry]() ### AI overview Gala migrated from Databricks to ClickHouse on AWS to address growing data volumes and improve analytics performance. The article reports a threefold increase in analytics capacity, a 30% cost reduction, and query times reduced from minutes to sub-second. ### Source excerpt Learn how Gala migrated to the ClickHouse to improve analytics performance and cut costs ## Smarter Ad Spending: 5 Marketing Analytics Strategies to Optimize Your Budget DevFeed: [Smarter Ad Spending: 5 Marketing Analytics Strategies to Optimize Your Budget]() Original publisher: [Read original article]() Author: KHALIL KAFROUNI, MARKETING EXPERT @ TOPTAL Published: 2026-04-15T04:00:00Z Content type: article Language: en Sources: [Toptal Blog]() Topics: [data analytics](), [Optimization](), [databricks]() Tags: [advertising](), [analytics](), [data](), [data-analytics](), [marketing](), [optimization]() ### AI overview This article presents five marketing analytics strategies for optimizing advertising budgets, reducing wasted spend, and improving marketing ROI through data-informed decision-making. ### Source excerpt Learn expert-backed strategies to optimize your advertising budget, avoid wasted spend, and drive higher marketing ROI through data-informed decision-making. ## Operating Trino at Scale With Trino Gateway DevFeed: [Operating Trino at Scale With Trino Gateway]() Original publisher: [Read original article]() Author: Prakhar Sapre Published: 2026-03-24T12:01:00Z Content type: article Language: en Sources: [Expedia]() Topics: [gateway](), [Load Balancing](), [Routing (disambiguation)](), [data analytics](), [Authentication](), [SQL]() Tags: [analytics](), [authentication](), [clusters](), [concurrency](), [data-science](), [downtime](), [gateway](), [load-balancer](), [load-balancing](), [open-source](), [sql](), [trino](), [trino-gateway](), [trinos]() ### AI overview This article explains how Trino Gateway routes queries across multiple Trino clusters and centralizes routing, authentication, load balancing, monitoring, and cluster management. It describes the project's origins as Presto Gateway at Lyft and its role in supporting larger analytics platforms with more complex workloads and higher concurrency. ### Source excerpt Expedia Group Technology -- DataWorkload-aware routing for TrinoPhoto by Joseph Barrientos on Unsplash Trino -- a fork of PrestoSQL -- is a powerful tool in modern data analytics, enabling organizations to query large datasets quickly and efficiently. As a distributed SQL query engine, Trino provides fast, scalable insights without requiring data relocation. While Trino is robust on its own, its capabilities are further enhanced when paired with a Gateway, which introduces features such as query routing, strong security, and streamlined cluster management. A brief overview The Gateway project originated at Lyft as Presto Gateway, serving as a proxy and load balancer for PrestoDB. It was later forked and integrated into the Trino ecosystem, with contributions from various organizations and the open-source community. The Gateway serves as a central point for managing and routing queries, providing a unified interface for users and administrators. As organizations scale their analytics platforms, they often encounter challenges such as increased query complexity, higher concurrency, and the need for specialized cluster configurations. Directing users to specific cluster endpoints becomes impractical as the user base grows. A Gateway addresses these challenges by routing queries to the most appropriate clusters based on workload, improving efficiency and responsiveness. The Gateway acts as a vital intermediary between users and the Trino query engine. By abstracting the complexities of distributed query execution, it manages critical functions such as routing, authentication, and load balancing across diverse backend clusters. This ensures that queries are efficiently directed to the optimal processing cluster. With an intuitive user interface, the Gateway transforms what was once a convoluted process into a manageable and transparent experience empowering administrators with real-time insights and precise control over their backend cluster infrastructure. Whether it's mon ## DigitalOcean Announces GPU Droplets Accelerated by NVIDIA HGX B300 DevFeed: [DigitalOcean Announces GPU Droplets Accelerated by NVIDIA HGX B300]() Original publisher: [Read original article]() Author: Waverly Swinton Published: 2025-12-15T17:51:51Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [GPU](), [Blackwell](), [Nvidia](), [virtual machines](), [High-Performance Computing](), [Inference](), [Cloud](), [Generative AI](), [data analytics](), [Multi-GPU](), [long-context]() Tags: [ai](), [blackwell](), [compute](), [digitalocean](), [gpu](), [high-performance-computing](), [inference](), [infrastructure](), [long-context](), [multi-gpu](), [nvidia](), [product-updates](), [real-time]() ### AI overview DigitalOcean announces GPU Droplets accelerated by NVIDIA HGX B300, describing the platform's intended benefits for AI training, inference, generative AI, data analytics, and high-performance computing workloads. ### Source excerpt AI continues to evolve at an unprecedented pace, with new models and demanding workloads pushing the boundaries of what's possible. From complex large language models (LLMs) to intricate scientific simulations, developers and businesses need access to the most powerful and efficient computing infrastructure. At DigitalOcean, we're committed to providing the cutting-edge tools you need to build, deploy, and scale your AI initiatives with simplicity and affordability. That's why we're excited to announce that GPU Droplets accelerated by NVIDIA HGX™ B300 are coming soon to DigitalOcean, marking a significant upgrade to our GPU offerings. Why NVIDIA HGX™ B300? The NVIDIA Blackwell Ultra accelerated computing platform represents a leap forward in AI reasoning. Designed for both training and inference, the NVIDIA HGX B300 offers substantial improvements in computational power, memory bandwidth, and energy efficiency compared to previous generations. The NVIDIA Blackwell architecture at the heart of the HGX B300 is not just about raw power; it's also about efficiency and innovation. With 1.5X more dense Tensor Core FLOPS, enhanced attention performance, and significantly expanded memory, the HGX B300 is optimized for the most demanding AI workloads including generative AI, data analytics, and high-performance computing (HPC). Featuring 7X more AI compute than NVIDIA Hopper platforms, 2.1TB of HBM3e memory, and high-performance networking integration with NVIDIA ConnectX-8 SuperNICs, Blackwell Ultra delivers breakthrough performance on the most complex workloads from agentic systems and reasoning, to real-time video generation. For AI-native enterprises running large reasoning models and long-context workloads, this enables: -Reduced model offloading and improved time-to-first-token -Higher sustained throughput under concurrency -More efficient multi-GPU scaling -Improved tokens-per-second per dollar Unlike GPU capacity providers, DigitalOcean integrates inference-optimized ## ClickHouse Partners with Japan Cloud to Establish ClickHouse K.K. and Accelerate Growth in Japan DevFeed: [ClickHouse Partners with Japan Cloud to Establish ClickHouse K.K. and Accelerate Growth in Japan]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-11-06T00:00:00Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data analytics](), [real-time](), [Data Infrastructure](), [Artificial Intelligence](), [Cloud]() Tags: [ai](), [analytics](), [clickhouse](), [cloud](), [data](), [data-infrastructure](), [growth](), [japan](), [partnership](), [real-time]() ### AI overview ClickHouse announced the establishment of ClickHouse K.K., its Japanese subsidiary, through a strategic partnership with Japan Cloud. The partnership is intended to expand ClickHouse's presence in Japan and help enterprises use real-time data to accelerate AI-driven innovation and growth. ### Source excerpt ClickHouse today announced the establishment of ClickHouse K.K., its Japanese subsidiary, through a strategic partnership with Japan Cloud. ## October 2025 newsletter DevFeed: [October 2025 newsletter]() Original publisher: [Read original article]() Author: Mark Needham Published: 2025-10-15T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [data analytics](), [migration](), [PostgreSQL](), [Apache Iceberg](), [observability]() Tags: [2025](), [clickhouse](), [data](), [data-analytics](), [data-processing](), [migration](), [newsletter](), [observability](), [postgresql](), [real-time](), [tools](), [training]() ### AI overview The October 2025 ClickHouse newsletter covers a new text index, request logging at billion-event scale, messy-data modeling for OLAP, lakehouse queries from ClickHouse Cloud, and related community and training events. ### Source excerpt Welcome to the October 2025 ClickHouse newsletter, which will round up what's happened in real-time data warehouses over the last month. ## What's new in ClickStack. August '25. DevFeed: [What's new in ClickStack. August '25.]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-08-20T12:50:49Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [SQL](), [Cloud](), [data analytics](), [real-time](), [ui](), [Traces](), [User Experience](), [Transactions]() Tags: [analytics](), [clickhouse](), [cloud](), [observability](), [real-time](), [sql](), [traces](), [transactions](), [ui](), [user-experience]() ### AI overview The August ClickStack update introduces Cloud-hosted HyperDX in ClickHouse Cloud, smarter search, dynamic visualizations, custom chart aliases, and SQL improvements. It presents ClickStack as an open-source observability stack that unifies logs, traces, metrics, real-time analytics, and data warehousing around ClickHouse. ### Source excerpt Discover what's new in ClickStack this month - from ClickStack in ClickHouse Cloud to smarter search, dynamic visualizations, SQL enhancements, and cutting-edge inverted index support that make observability faster and more powerful than ever. ## Streaming optimized data to S3 for analytics with Parquet DevFeed: [Streaming optimized data to S3 for analytics with Parquet]() Original publisher: [Read original article]() Author: Chandler Mayo Published: 2025-08-13T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [parquet](), [Redpanda-Connect](), [Amazon S3](), [data analytics](), [Streaming](), [Apache Spark](), [pandas](), [Serverless]() Tags: [analytics](), [analytics-systems-data-feed](), [apache-spark-and-athena-data-queries](), [athena](), [automating-data-pipelines-in-s3](), [building-dashboards-with-s3-data](), [compressing-data-with-parquet](), [data](), [data-pipeline](), [data-pipeline-for-analytics](), [event-driven-pipelines-with-s3](), [how-to](), [learn](), [pandas](), [parquet](), [parquet-files-in-amazon-s3](), [pipeline](), [real-time-data-streaming](), [redpanda-connect](), [redpanda-data-streaming](), [s3](), [serverless](), [setting-up-redpanda-observability](), [spark](), [streaming](), [streaming-data-to-s3-with-parquet](), [tutorial]() ### AI overview This tutorial explains how to use Redpanda Connect to continuously batch streaming data and write compressed Apache Parquet files to Amazon S3 for analytical workloads. It covers the benefits of Parquet and querying the resulting files with tools such as Pandas, Apache Spark, and Athena. ### Source excerpt Learn how to build a powerful data pipeline that feeds analytics systems from Redpanda using clean, compressed Parquet files in Amazon S3. ## SEON Achieves 80% Faster Database Performance for Anti-Fraud Platform with ClickHouse on AWS DevFeed: [SEON Achieves 80% Faster Database Performance for Anti-Fraud Platform with ClickHouse on AWS]() Original publisher: [Read original article]() Author: AWS Published: 2025-07-23T09:16:59Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Amazon Web Services (AWS)](), [Database](), [data analytics](), [real-time](), [Scalability](), [Security, Privacy and Abuse Prevention](), [API](), [Machine learning]() Tags: [analytics](), [api](), [aws](), [clickhouse](), [data](), [data-analytics](), [database](), [database-performance](), [fraud](), [machine-learning](), [performance](), [real-time](), [scalability](), [storage]() ### AI overview SEON worked with ClickHouse and AWS to improve the performance and scalability of its anti-fraud and anti-money-laundering platform. The solution reduced processing time by more than 80 percent while supporting near-real-time transaction analysis and large, customized rule sets. ### Source excerpt Learn how anti-fraud platform SEON worked with ClickHouse and used AWS to improve near real-time data analytics by 80% and reduce data storage costs by 30% ## Turning Spain's blackout into real-time insights DevFeed: [Turning Spain's blackout into real-time insights]() Original publisher: [Read original article]() Author: Iago Enríquez Published: 2025-07-11T10:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [data analytics](), [real-time](), [data]() Tags: [analytics](), [data](), [data-analytics](), [i-built-this](), [real-time]() ### AI overview The article examines how real-time data analytics could help understand the work of the electric grid in the context of Spain's blackout. ### Source excerpt How real data analytics could help to understand electric grid's work ## Measuring Commercial Impact at Scale at Canva DevFeed: [Measuring Commercial Impact at Scale at Canva]() Original publisher: [Read original article]() Author: Jun Ye Published: 2025-06-20T00:00:01Z Content type: article Language: en Sources: [Canva Engineering]() Topics: [experiments](), [Streamlit](), [data analytics](), [App](), [Software as a service]() Tags: [data-analytics](), [development](), [experiment](), [experimentation](), [metrics](), [product](), [streamlit]() ### AI overview Canva describes its IMPACT app, an internal data product built with Snowflake, Streamlit, Snowpark, and Cortex. The app provides self-service estimates of commercial impact for experiment analysis using standardized logic aligned with Canva's finance model. ### Source excerpt How We Built Canva's IMPACT App with Streamlit in Snowflake ## March 2025 Newsletter DevFeed: [March 2025 Newsletter]() Original publisher: [Read original article]() Author: Mark Needham Published: 2025-03-20T00:00:00Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [Apache Iceberg](), [Data Infrastructure](), [data analytics](), [Large Language Model](), [Traces](), [Grafana](), [BigQuery]() Tags: [2025](), [apache-iceberg](), [aws](), [bigquery](), [clickhouse](), [conference](), [data](), [data-analytics](), [data-infrastructure](), [data-platforms](), [llms](), [newsletter](), [observability](), [real-time](), [traces](), [training]() ### AI overview The March 2025 ClickHouse newsletter covers recent product updates, including the Postgres CDC connector for ClickPipes entering Public Beta and general availability for Bring Your Own Cloud on AWS. It also highlights ClickHouse support for Apache Iceberg, data warehouse use cases, community work on an AI gateway using ClickHouse, and upcoming events, training, and workshops. ### Source excerpt Welcome to the March ClickHouse newsletter, which will round up what's happened in real-time data warehouses over the last month. ## How I created my first Course with Datacamp DevFeed: [How I created my first Course with Datacamp]() Original publisher: [Read original article]() Published: 2025-01-22T00:00:00Z Content type: article Language: en Sources: [Mutt0-ds Notes]() Topics: [Microsoft](), [Security](), [Deployment](), [configuration](), [version](), [version-control](), [data analytics]() Tags: [configuration](), [course](), [data-analytics](), [deployment](), [microsoft](), [security](), [version]() ### AI overview The author describes creating and publishing a DataCamp course on advanced Microsoft Fabric topics, including security, configuration, version control, and deployment. The article explains how DataCamp's Fabric sandbox supports hands-on exercises and outlines the course-development process. ### Source excerpt I just published an online course! covering advanced Microsoft Fabric topics like security, deployment, admin settings, and version... ## Trino Summit 2024 resources DevFeed: [Trino Summit 2024 resources]() Original publisher: [Read original article]() Author: Manfred Moser, Monica Miller, Anna Schibli Published: 2024-12-18T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [data](), [data analytics](), [interoperability](), [text2sql](), [Kubernetes](), [Resilience](), [Security](), [Persistence](), [Artificial Intelligence]() Tags: [analytics](), [community](), [data](), [interoperability](), [kubernetes](), [resilience](), [resources](), [security](), [slack](), [software-engineer](), [summit]() ### AI overview A recap of Trino Summit 2024 sessions covering large-scale data warehouses, data lakes and lakehouses, self-service analytics, text-to-SQL, interoperability, cost efficiency, observability, policy enforcement, and Trino on Kubernetes. ### Source excerpt What a view we had at the summit! Over 700 live attendees enjoyed the sessions and learned more about Trino-related use cases and projects. Now it is time for the additional 1000 registrants, our 13000+ Trino users on Slack, and everyone else in the Trino community and beyond to enjoy the presentations and recordings at their leisure. ## The glorious lineup for Trino Summit 2024 DevFeed: [The glorious lineup for Trino Summit 2024]() Original publisher: [Read original article]() Author: Manfred Moser, Monica Miller, Anna Schibli Published: 2024-11-22T00:00:00Z Content type: news Language: en Sources: [Trino Blog]() Topics: [SQL](), [Artificial Intelligence](), [Kubernetes](), [Apache Iceberg](), [data analytics](), [text2sql](), [observability](), [Security](), [Resilience]() Tags: [2025](), [ai](), [data-analytics](), [event](), [free](), [kubernetes](), [observability](), [resilience](), [security](), [sql](), [summit]() ### AI overview The article announces the full lineup for the free, virtual, two-day Trino Summit 2024. It highlights a keynote on Trino project and community developments, a panel on AI and Trino, and sessions covering Kubernetes, observability, data lakes with Iceberg, policy enforcement, and text-to-SQL analytics. ### Source excerpt We just wrapped up our mini training series SQL basecamps before Trino Summit, and now Trino Summit 2024 is less than three busy weeks away. It's a good thing that we have also been working hard on all the preparations for the summit. Everything is coming together, and we are excited to share the full lineup for the free, virtual, two day event today. ## A glimpse at the summit DevFeed: [A glimpse at the summit]() Original publisher: [Read original article]() Author: Manfred Moser, Monica Miller, Anna Schibli Published: 2024-10-17T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [data analytics](), [Apache Iceberg](), [Data Space](), [Scalability](), [SQL](), [Open Source]() Tags: [analytics](), [apache-iceberg](), [event](), [free](), [interoperability](), [netflix](), [open-source](), [scalability](), [sql](), [training](), [virtual-event]() ### AI overview A preview of the two-day, free, virtual Trino Summit 2024 highlights proposed sessions on large-scale Trino deployments, Apache Iceberg, lakehouse architectures, data platform interoperability, analytics cost efficiency, performance, and scalability. ### Source excerpt Our efforts around Trino Summit 2024 are ramping up and the event is creeping closer and closer. We are really looking forward to the two-day, free, virtual event in December about all things Trino. While we are working hard to put together the SQL basecamps before Trino Summit training sessions and other community events, a number of your awesome peers from the Trino community submitted session proposals, and we are excited to share that glimpse on the agenda for Trino Summit 2024. ## How to break into data in 2024? With DataCamp's CEO, Jonathan Cornelissen. DevFeed: [How to break into data in 2024? With DataCamp's CEO, Jonathan Cornelissen.]() Original publisher: [Read original article]() Author: Meri Nova Published: 2024-10-09T17:31:09Z Content type: opinion Language: en Sources: [Meri Nova]() Topics: [Data Science](), [data analytics](), [genai](), [Artificial Intelligence](), [DataOps]() Tags: [ai](), [beginners](), [career-advice](), [data](), [data-analytics](), [data-science](), [education](), [future-of-work](), [genai](), [podcast](), [scaling](), [technical]() ### AI overview A Technical Founder podcast episode featuring DataCamp co-founder and CEO Jonathan Cornelissen. The discussion covers building DataCamp, entering data careers, data education, GenAI's impact on edtech, data literacy, and career advice for new graduates. ### Source excerpt Listen to our first episode of the "Technical Founder" podcast, where we invite AI and Data leaders to learn from their entrepreneurial and technical journey! ## Extend the power of your AppSec data with the new Snyk and Snowflake integration DevFeed: [Extend the power of your AppSec data with the new Snyk and Snowflake integration]() Original publisher: [Read original article]() Author: Guy Plotnik Published: 2024-08-06T05:00:00Z Content type: news Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [data analytics](), [dashboards](), [data](), [API]() Tags: [analytics](), [application-security](), [awareness](), [blog](), [customers](), [datasets](), [devops](), [enablement](), [enterprise](), [executive](), [insights](), [integration](), [interest](), [latency](), [platform](), [pmm](), [related-content](), [security](), [snyk](), [snyk-platform]() ### AI overview Snyk introduces Snyk Analytics for Snowflake, enabling customers to access and analyze Snyk data directly from Snowflake through secure data sharing. The integration supports AppSec analytics, custom analysis, data joins, dashboards, and faster access without ETL pipelines. ### Source excerpt We are thrilled to announce the new Snyk Analytics for Snowflake, allowing Snyk customers to seamlessly access and analyze Snyk's data from their Snowflake account. ## 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 [Next page]()