Remove Data Processing Remove Metadata Remove Modeling
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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

EUROGATEs data science team aims to create machine learning models that integrate key data sources from various AWS accounts, allowing for training and deployment across different container terminals. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog. This process is shown in the following figure.

IoT 100
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Demystify data sharing and collaboration patterns on AWS: Choosing the right tool for the job

AWS Big Data

As a producer, you can also monetize your data through the subscription model using AWS Data Exchange. To achieve this, they plan to use machine learning (ML) models to extract insights from data. Next, we focus on building the enterprise data platform where the accumulated data will be hosted.

Sales 104
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Modernize your legacy databases with AWS data lakes, Part 2: Build a data lake using AWS DMS data on Apache Iceberg

AWS Big Data

format(dbname, table_name)) except Exception as ex: print(ex) failed_table = {"table_name": table_name, "Reason": ex} unprocessed_tables.append(failed_table) def get_table_key(host, port, username, password, dbname): jdbc_url = "jdbc:sqlserver://{0}:{1};databaseName={2}".format(host, To start the job, choose Run. format(dbname)).config("spark.sql.catalog.glue_catalog.catalog-impl",

Data Lake 100
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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

This model balances node or domain-level autonomy with enterprise-level oversight, creating a scalable and consistent framework across ANZ. The Institutional Data & AI platform adopts a federated approach to data while centralizing the metadata to facilitate simpler discovery and sharing of data products.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO Business Intelligence

Generative artificial intelligence ( genAI ) and in particular large language models ( LLMs ) are changing the way companies develop and deliver software. The commodity effect of LLMs over specialized ML models One of the most notable transformations generative AI has brought to IT is the democratization of AI capabilities.

Software 128
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How BMW streamlined data access using AWS Lake Formation fine-grained access control

AWS Big Data

The CDH is used to create, discover, and consume data products through a central metadata catalog, while enforcing permission policies and tightly integrating data engineering, analytics, and machine learning services to streamline the user journey from data to insight.

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. For machine learning systems used in consumer internet companies, models are often continuously retrained many times a day using billions of entirely new input-output pairs.