InTDS ArchivebyPrukalpa5 Trends Driving the New World of Metadata in 2022These trends have converged to create a storm around a new, modern idea of metadata.Feb 2, 2022Feb 2, 2022
Luigi ScappinWhy an Event Driven architecture should be Data CentricAlso for making the AI more actionable… in production!Mar 31, 2021Mar 31, 2021
InCreative DatabyPatrick PichlerDistributed Query Engines vs. Data Lake EnginesThe evolution from SQL-based query engines for big data to data lake engines including its impact on data warehouses and data lakesNov 24, 2020Nov 24, 2020
InTDS ArchivebyFarhan SiddiquiWhy and When to Avoid S3 as a Data Platform for Data LakesData lakes are all the rage these days in large enterprises. A data lake is a single store for both raw copies of source system data and…Mar 24, 202014Mar 24, 202014
InData Analyst BlogbyMkrtich PudeianThe most common mistakes of ETL developersby Mkrtich PudeianOct 15, 20201Oct 15, 20201
InTDS ArchivebyAndy McDonaldLoading Well Log Data From DLIS using PythonA short tutorial on loading and displaying processed acoustic waveform data using dlisio and matplotlibApr 21, 20212Apr 21, 20212
InDataSeriesbyJesus RodriguezInside the Architecture Powering Data Quality Management at UberData Quality Monitor implements novel statistical methods for anomaly detection and quality management in large data infrastructures.Feb 10, 20211Feb 10, 20211
InML6teambyKoen VerschaerenOpinion — How to build a data architecture to drive innovation — today and tomorrowIn this opinion we review a report by McKinsey called “How to build a data architecture to drive innovation — today and tomorrow”.Feb 11, 2021Feb 11, 2021
InTDS ArchivebyPrukalpaThe Building Blocks of a Modern Data PlatformThe collection of tools and capabilities that should be part of your data platform todayFeb 22, 20216Feb 22, 20216
InTDS ArchivebyLuca BigonThe modern data patternReplyable data processing and ingestion at scale with serverless, Snowflake and dotJan 21, 20222Jan 21, 20222
InData ArenabyJoão Vazao VasquesBuilding Data Platforms III — The evolution of the Software EngineerOriginally, I wanted to launch this article one month after part II. However, it took a lot longer for various reasons. Work got in the…Dec 29, 2021Dec 29, 2021
The Data WallWhat I Think When I Talk About DataEvery year, I set a physical and mental goal for myself on my birthday. These goals are essentially “stretch” goals and are meant to…Dec 12, 2021Dec 12, 2021
InTDS ArchivebyEric BrodaData Mesh Patterns: Change Data CaptureData Mesh uses the Change Data Capture (CDC) pattern to move data reliably around the enterprise. This is a deep dive on the CDC pattern.Jan 26, 20223Jan 26, 20223
InCodeXbyChristianlauerData Lakehouse vs. Data LakeWhat are the Differences and how they are build up on each otherMar 25, 20222Mar 25, 20222
InIBM Data Science in PracticebyChristian BerneckerETL Pipelines & Data Preparation for any skill level with Cloud Pak for DataThe Extract, Transform and Load pattern (ETL) is a classic in Data Engineering, but it’s still the most common and useful. An ETL can be…Aug 3, 20221Aug 3, 20221
InTDS ArchivebyBarr MosesDecoding the Data MeshBuilding a data mesh? Avoid these common mesh-conceptions.Aug 12, 2021Aug 12, 2021
InTDS ArchivebyJoão António SousaBreaking 6 Analytics Habits to Unlock ValueWhat can teams do to improve their data journeyOct 22, 20223Oct 22, 20223
InTDS ArchivebyBarr MosesThe Ultimate Data Observability ChecklistData observability is more than just setting up a bunch of data pipeline tests and hoping for the best.Mar 10, 2021Mar 10, 2021
LAKSHMI VENKATESHData Technology Trend #2: Strategic (Part 4)This article is a part of a multi-part series Data Technology Trends (parent article). Previous article Data Technology Trend # 1: Trusted…Jun 11, 2021Jun 11, 2021
