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  1. Stop the "Small File Syndrome" in your Data Lake. Learn how to implement Compaction, Z-Ordering, and automated maintenance in Databricks and Snowflake. hackernoon.com/the-silent-kill #datalake

  2. Stop the "Small File Syndrome" in your Data Lake. Learn how to implement Compaction, Z-Ordering, and automated maintenance in Databricks and Snowflake. hackernoon.com/the-silent-kill #datalake

  3. Stop the "Small File Syndrome" in your Data Lake. Learn how to implement Compaction, Z-Ordering, and automated maintenance in Databricks and Snowflake. hackernoon.com/the-silent-kill

  4. Stop the "Small File Syndrome" in your Data Lake. Learn how to implement Compaction, Z-Ordering, and automated maintenance in Databricks and Snowflake. hackernoon.com/the-silent-kill #datalake

  5. #Uber’s HiveSync team optimized Hadoop Distcp for multi-petabyte replication across hybrid cloud and on-prem data lakes.

    ✅ Task parallelization
    ✅ Uber jobs for small transfers
    ✅ Improved observability

    Result: 5× replication capacity & seamless on-prem-to-cloud migration.

    Read more: bit.ly/4bwUUFt

    #InfoQ #SoftwareArchitecture #DistributedSystems #Observability #DataLake

  6. Data lakes are often thought of as just warehouses. But they don't have to be! Our #datalake provides inexpensive storage where logs stay searchable, preview-able & recoverable. Learn more about why this is a truly practical stance on managing data volume. graylog.org/post/how-to-... #CyberSecurity

    How to Use Data Lakes to Reduc...

  7. Automated product metrics monitoring on Google Cloud Platform using BigQuery and Cloud Functions for analysis and anomaly detection. hackernoon.com/why-our-analyst #dataplatform

  8. Data & Corpus – La revue des données en SHS a le plaisir de vous annoncer la parution en ligne de son premier numéro entièrement consacré aux articles de données (data papers) : dc.episciences.org/volumes/1042

    #datapaper
    #scienceouverte
    #shs
    #DiamondOpenAccess

  9. Bildungsgeschichte.de ist jetzt auch auf Mastodon. Wir freuen uns, Sie hier über neue Kolumnen und #Datapaper auf bildungsgeschichte.de/index.ph zu informieren
    #histed #histodons #digitalhistory #dh

  10. 🆕 #DataPaper describes a publicly available #dataset related to 752 community tutelary shrines in 🇹🇼 #Taiwan, and establishes a baseline for future #research into #culturalheritage.

    🗨 "As community #ritual assemblages, they are able to encode #data about a settlement’s #social, #political and #economic #history in their material composition, aesthetic choices, artefacts, displays and orientations."

    👉 See: doi.org/10.3897/rio.10.e127510

    #humanities #socialscience

  11. This is a bit of an odd duck as a #DataPaper. Traditional research articles showcase some new development in the field and connect it to a reproducible line of evidence. #DataPapers on the other hand are relatively new and focus on the data collection where the data itself is the main development. The promise here is that the data is broad enough and robust enough to be of general interest to other researchers... let's dig in. 3/n

  12. bildungsgeschichte.de ist nach einer längeren technisch bedingten Abwesenheit wieder online. Es gibt zwar noch technische Einschränkungen, aber auch ein neues #datapaper von Maret Nieländer: "Historische Schulbücher mit digitalen Werkzeugen untersuchen"
    doi.org/10.25523/32552.a

  13. CW: A second chance awaits for sharing #OpenData #FAIRdata on #OneHealth #biodiversity related to human vector-borne diseases.

    Prepare your dataset on wild vectors of human diseases, draft submit your #dataPaper by 30 April, and—if @GigaScience's #GigaByteJournal accepts your manuscript, #TDR at @WHO will pick up the US$400 article processing charge!
    gbif.org/vectors-call2

  14. Dataland, the world’s first AI art museum, will open in 2025 at Frank Gehry’s The Grand LA in downtown Los Angeles. The museum, led by Refik Anadol Studio, will showcase immersive AI-driven art. @refikanadol

    #refikanadol #dataland #museum #dailyartnews

    buff.ly/4eFqFMg

  15. Refik Anadol Studio introduces DATALAND - A Web3 platform merging AI arts and environmental advocacy (in their own words).
    A "Large Nature Model", an innovative tool for environmental awareness.
    dataland.art/
    #DATALAND #LargeNatureModel

  16. #Dataland
    Pour ceux qui préfèrent le ⬇️ télécharger (3.17GB) en utilisant un client torrent, voici lien magnet :

    framabin.org/p/?fced85ef07e684

  17. The team have over the last few weeks been focused on some Data Platform projects, Hydrology enhancements, Regulated Products enhancements and some of our own technology product updates.

    Thank you everyone that we’ve met with over the last few week.
    www.epimorphics.com #DataPlatform #ThisWeek #DataSolutions #DataDriven #LinkedData #Hydrology #RegulatedProducts

  18. Want to implement CI/CD for Microsoft Fabric? On 2026-02-19, Kev Chant walks us through Azure DevOps integration with Fabric. We will be covering Git workflows, branching strategies, and deployment approaches for Data Warehouses and SQL databases. meetup.com/fabricpowerbiwales/
    #MicrosoftFabric #AzureDevOps #DataPlatform

  19. We’ve updated a number of our core products including #DataPlatform, Agora #DataCatalog, #MeasurementStore, & #ConceptStore + other #reference #DataManagement tools. Looking for #ConnectedData tech to support your #DataArchitecture then we’d love to talk. www.epimorphics.com

  20. [登壇レポート]Apache Icebergと超えていくデータレイクの限界 -S3とSnowflake活用事例-でSnowflake×Icebergの機能と活用例についてお話しました #datalake_findy
    dev.classmethod.jp/articles/sp

    #dev_classmethod #Snowflake #Apache_Iceberg

  21. @stephensmith With the exponential expansion of info locked up in unstructured data in the form of images, video, audio, documents, using the traditional monolithic data warehouse based on system generated normalised data to gain organisational insights (via the so-called Inmon method) became rather outmoded #DataLakes #DataWarehouse #UnstructuredData

  22. @stephensmith data warehouses store _structured_ data i.e. traditional, rigid table / row / columnar information usually produced by systems, for purposes of read-heavy analytics processing, e.g. customer data with cust id, address lines, name, forename, phone numbers, etc; data _lakes_ allow mass storage of _unstructured_ data, usually generated by humans, also for the purposes of analytics processing. #DataLakes #DataWarehouse #UnstructuredData