Remove Building Remove Data Schemas Remove Demo
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Building a Machine Learning Application With Cloudera Data Science Workbench And Operational Database, Part 3: Productionization of ML models

Cloudera

In this last installment, we’ll discuss a demo application that uses PySpark.ML to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. As a result, I decided to use an open-source Occupancy Detection Data Set to build this application.

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Improving Meta’s global maps

Engineering at Meta

We’re Meta now, but our mission remains the same: Giving people the power to build community and bring the world closer together. This new data schema was born partly out of our cartographic tiling logic, and it includes everything necessary to make a map of the world. Icon versus icon Our initial basemaps eschewed icons.

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DataMynd: Empowering Data Teams with Native Data Privacy Solutions

Snowflake

Welcome to Snowflake’s Startup Spotlight, where we ask startup founders about the problems they’re solving, the apps they’re building, and the lessons they’ve learned during their startup journey. It’s basically an “easy button” for synthetic data. You can even train ML models on our synthetic data, or use it for data sharing purposes.

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How to Easily Connect Airbyte with Snowflake for Unleashing Data’s Power?

Workfall

Pre-filter and pre-aggregate data at the source level to optimize the data pipeline’s efficiency. Adapt to Changing Data Schemas: Data sources aren’t static; they evolve. Account for potential changes in data schemas and structures.

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A Guide to Data Pipelines (And How to Design One From Scratch)

Striim

In this guide, we’ll dive into everything you need to know about data pipelines—whether you’re just getting started or looking to optimize your existing setup. We’ll answer the question, “What are data pipelines?” Then, we’ll dive deeper into how to build data pipelines and why it’s imperative to make your data pipelines work for you.

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17 Ways to Mess Up Self-Managed Schema Registry

Confluent

Therefore, not restricting access to the Schema Registry might allow an unauthorized user to mess with the service in such a way that client applications can no longer be served schemas to deserialize their data. Allow end user REST API calls to Schema Registry over HTTPS instead of the default HTTP.

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Optimizing Kafka Streams Applications

Confluent

When building a topology with the Processor API, you explicitly name each processing node in the topology, and also provide the name(s) of all of its parent nodes (the only exception are source nodes, which do not have any parents). .< build(properties); final KafkaStreams streams = new KafkaStreams(topology, properties); streams.

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