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Beyond “Prompt and Pray”

O'Reilly on Data

When we talk about conversational AI, were referring to systems designed to have a conversation, orchestrate workflows, and make decisions in real time. Its quick to implement and demos well. The prompt-and-pray approach is tempting because it demos well and feels fast.

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How to Implement Data Engineering in Practice?

Analytics Vidhya

Components of Data Engineering Object Storage Object Storage MinIO Install Object Storage MinIO Data Lake with Buckets Demo Data Lake Management Conclusion References What is Data Engineering? Image Source: GitHub Table of Contents What is Data Engineering? Initially, we have the definition of Software […].

Data Lake 391
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Someone hacks together a quick demo with ChatGPT and LlamaIndex. The system is inconsistent, slow, hallucinatingand that amazing demo starts collecting digital dust. Check out the graph belowsee how excitement for traditional software builds steadily while GenAI starts with a flashy demo and then hits a wall of challenges?

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Simplify your query performance diagnostics in Amazon Redshift with Query profiler

AWS Big Data

For more information, refer to Amazon Redshift clusters. However, if you would like to implement this demo in your existing Amazon Redshift data warehouse, download Redshift query editor v2 notebook, Redshift Query profiler demo , and refer to the Data Loading section later in this post. Run cell #12.

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Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog

AWS Big Data

For more detailed configuration, refer to Write properties in the Iceberg documentation. Replace with your database name, with your table name, amzn-s3-demo-bucket with your S3 bucket name. These conflicts are typically transient and can be automatically resolved through retries. config(f"spark.sql.catalog. groupBy("value").agg(max_("timestamp").alias("timestamp"))

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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

Prerequisites Complete the following prerequisites before setting up the solution: Create a bucket in Amazon S3 called zero-etl-demo- - (for example, zero-etl-demo-012345678901-us-east-1 ). Create an AWS Glue database , such as zero_etl_demo_db and associate the S3 bucket zero-etl-demo- - as a location of the database.

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Defining Simplicity for Enterprise Software as “a 10 Year Old Can Demo it”

Cloudera

During the development of Operational Database and Replication Manager, I kept telling folks across the team it has to be “so simple that a 10 year old can demo it”. so simple that a 10 year old can demo it”. Watch this: Enterprise Software that is so easy a 10 year old can demo it.