Stacksync (YC W24)’s Post

Qlik didn't tell me there were going to be multiple #QlikCloud feature releases today! On top of the container updates I mentioned in my previous post there's also... 🎫 Native JSON support. Whilst it's been supported through the REST connector for a long time, you couldn't just load a JSON file in to #QlikSense... until now. (In case you missed it, Parquet support was added about 18 months ago.) 🕺🏻 You can now style the selections bar when using themes. You no longer have to be green-white-grey but can now be Ometis Limited orange-orange-orange if you really want! 🧱 Continued stability improvements for Qlik Data Gateway Direct Access. 🔴 Transformation flows now support Amazon Redshift 🚮 Cascading delete of Data Integration projects Aaaaand breathe... I think that's all for today! #QlikPartnerAmbassador

🌐 Unlocking Data Efficiency with BigQuery with Apache Iceberg 🚀 Google Cloud’s BigQuery support for Apache Iceberg tables is a game-changer for organizations looking to scale their data architecture. This integration brings powerful benefits: Cost Efficiency: Advanced partition pruning reduces query costs. Schema Evolution: Seamlessly adapt to changing data needs without breaking queries. Open Standards: Flexibility to use tools across platforms with Iceberg’s open format. Unified Data Lake + Warehouse: Manage structured and unstructured data in one place. For businesses seeking scalable, flexible, and cost-efficient solutions to manage large datasets, BigQuery + Apache Iceberg offers a compelling path forward. 🌟 Check out our blog for the full story and explore more at KrossKut! Find the link below! Full Blog: https://lnkd.in/gyzmY4vt Our Website: https://krosskut.io/ Let’s stay in touch! Join our social media community to stay informed and engaged with our latest initiatives 👇 YouTube: https://lnkd.in/gNVsgyqK Facebook: https://lnkd.in/gr2k9CPw X: https://x.com/krosskutio Instagram: https://lnkd.in/gpjvCF_R Threads: https://lnkd.in/gkQ3cSM6 Pinterest: https://lnkd.in/ggjCZpjv #DataManagement #BigQuery #ApacheIceberg #GoogleCloud #DataEfficiency #CloudData

#BigQuery #CrossCloud feature in #GA : A materialized view replica lets you use the #AmazonS3 materialized view data in queries while avoiding data egress costs and improving query performance. A materialized view replica does this by replicating the #Amazon S3 data to a dataset in a supported #BigQuery region, so that the data is available locally in #BigQuery. Can be achieved using simple SQL statement below, CREATE MATERIALIZED VIEW PROJECT_ID.BQ_DATASET.REPLICA_NAME OPTIONS(replication_interval_seconds=REPLICATION_INTERVAL) AS REPLICA OF PROJECT_ID.S3_DATASET.MATERIALIZED_VIEW_NAME;

Why is BigQuery a game-changer? No capacity concerns, limitless storage, and you pay only for the queries you run. Build without limits, BigQuery handles the heavy lifting seamlessly. Catch the full conversation here: bit.ly/4eWUBnf Join Rahul Kumar as he breaks down how BigQuery is transforming data management in our latest discussion! #TechTrends2024 #CloudComputing #Serverless #BigQuery #AutomationTools #AntStack #AppModernization #TechWednesday

🚀 How DuckDB can be up to 1000x more efficient than BigQuery? 🚀 In his Medium article, Christophe O. dives into how DuckDB, an embedded database engine, can outperform BigQuery in specific use cases, achieving up to 1000x efficiency. 🔥 👉 Explore: - Local queries in DuckDB vs cloud-based queries in BigQuery - Resource optimization through columnar storage 📖 Read the full article here: https://lnkd.in/dBwqqp6a #DataScience #BigData #DuckDB #BigQuery #Optimization #Technology #Efficiency

Get an introduction to Distributed SQL at scale! Register Now: https://hubs.ly/Q02WfDyr0 Join us tomorrow 10:00am GMT for an exclusive webinar on TiDB—the open-source, distributed SQL database that combines the best of SQL and NoSQL. In this session, PingCAP Senior Solution Engineer Matthew Penaroza will cover: ✅ Scaling transactional workloads with ACID compliance ✅ Success stories from Pinterest, Databricks, LinkedIn ✅ TiDB’s architecture benefits: fault tolerance & resource efficiency ✅ Key use cases in Finance, E-Commerce, Gaming, SaaS #Database #Scalability #DataIntegrity #SQL #NoSQL #TiDB

Even though Databricks data apps are in preview, I managed to create a CI/CD pipeline to manage apps in a workspace. The pipeline is for all the frameworks, can have several apps and grants access to sql endpoint. The only things missing is access management to catalog. If you have any scripts that can automate granting apps to catalogs, schemas, and tables, would love to collaborate. https://lnkd.in/dRev7dcm

The latest update for #BigQuery includes "Get up to 100x query performance improvement with BigQuery history-based optimizations" and "BigQuery tables for Apache Iceberg: optimized storage for the open lakehouse". #bigdata #analytics https://lnkd.in/dQP5hVbe

🧠 You can eliminate the need for data movement and separate infrastructure management by running #ML models directly within your bigquery data warehouse. Experience unparalleled convenience and scalability for both simple and complex tasks. #BigQuery #DataScience #MachineLearning #DataWarehouse Friday's #CloudSecrets by Yoram 🙏 Do you wanna publish yours? Let us know! #DoYouHaveACloud☁