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050 0 0 _aQA76.9.D343
_bA235 2021
082 0 4 _a006.312
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_bMAC-9
245 0 0 _a97 things every data engineer should know :
_bcollective wisdom from the experts
_cedited by Tobias Macey
246 3 _aNinety-seven things every data engineer should know
246 3 0 _aThings every data engineer should know
260 _aMumbai :
_bShroff Publishers,
_c©2021
300 _axiv, 248 p. :
_bill. ;
_c24 cm
500 _aIncludes index.
504 _aIncludes bibliographical references and index.
505 2 0 _tA (book) case for eventual consistency /
_rDenise Koessler Gosnell, PhD --
_tA/B and how to be /
_rSonia Mehta --
_tAbout the storage layer /
_rJulien Le Dem --
_tAnalytics as the secret glue for microservice architectures /
_rElias Nema --
_tAutomate your infrastructure /
_rChristiano Anderson --
_tAutomate your pipeline tests /
_rTom White --
_tBe intentional about the batching model in your data pipelines /
_rRaghotham Murthy --
_tBeware of silver-bullet syndrome /
_rThomas Nield --
_tBuilding a career as a data engineer /
_rVijay Kiran --
_tBusiness dashboards for data pipelines /
_rValliappa (Lak) Lakshmanan --
_tCaution : data science projects can turn into the emperor's new clothes /
_rShweta Katre --
_tChange data capture /
_rRaghotham Murthy --
_tColumn names as contracts /
_rEmily Riederer --
_tConsensual, privacy-aware data collection /
_rKatharine Jarmul --
_tCultivate good working relationships with data consumers /
_rIdo Shlomo --
_tData engineering !=Spark /
_rJesse Anderson --
_tData engineering for autonomy and rapid innovation /
_rJeff Magnusson --
_tData engineering from a data scientist's perspective /
_rBill Franks --
_tData pipeline design patterns for reusability and extensibility /
_rMukul Sood --
_tData quality for data engineers /
_rKatharine Jarmul --
_tData security for data engineers /
_rKatharine Jarmul --
_tData validation is more than summary statistics /
_rEmily Riederer --
_tData warehouses are the past, present, and future /
_rJames Densmore --
_tDefining and managing messages in log-centric architectures /
_rBoris Lublinsky --
_tDemystify the source and illuminate the data pipeline /
_rMeghan Kwartler --
_tDevelop communities, not just code /
_rEmily Riederer --
_tEffective data engineering in the cloud world /
_rDipti Borkar --
_tEmbracing data silos /
_rBin Fan and Amelia Wong --
_tEngineering reproducible data science projects /
_rDr. Tianhui Michael Li --
_tFive best practices for stable data processing /
_rChristian Lauer --
_tFocus on maintainability and break up those ETL tasks /
_rChris Moradi
520 _aTake advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular "Data engineering podcast", this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers.--
650 0 _aBig data.
650 0 _aData sets.
650 0 _aData collection platforms.
650 0 _aDatabases.
650 0 _aElectronic data processing.
650 2 _aDatasets as Topic
650 6 _aDonnées volumineuses.
650 6 _aJeux de données.
650 6 _aPlateformes de collecte de données.
650 7 _aData collection platforms.
_2fast
650 7 _aData sets.
_2fast
700 1 _aMacey, Tobias
_eeditor
906 _a7
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