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Streaming Big Data Files from Cloud Storage

Towards Data Science

In such cases one must consider the manner in which the files will be pulled to the application while taking into account: bandwidth capacity, network latency, and the application’s file access pattern. This continues a series of posts on the topic of efficient ingestion of data from the cloud (e.g.,

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Introducing Compute-Compute Separation for Real-Time Analytics

Rockset

When you deconstruct the core database architecture, deep in the heart of it you will find a single component that is performing two distinct competing functions: real-time data ingestion and query serving. When data ingestion has a flash flood moment, your queries will slow down or time out making your application flaky.

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Google Cloud Pub/Sub: Messaging on The Cloud

ProjectPro

Data engineers often use Google Cloud Pub/Sub to design asynchronous workflows, publish event notifications, and stream data from several processes or devices. This blog provides an overview of Google Cloud Pub/Sub that will help you understand the framework and its suitable use cases for your data engineering projects.

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Controlling Cloud Costs for the Ascend Platform

Ascend.io

Understanding and controlling cloud costs is a fundamental part of how Ascend manages the cloud infrastructure of our dedicated deployment customers. These are customers where the entire Ascend software stack is installed in their cloud account. Compute : This refers to all processes responsible for actually handling your data.

Cloud 52
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A Cost-Effective Data Warehouse Solution in CDP Public Cloud – Part1

Cloudera

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

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Accelerate your Data Migration to Snowflake

RandomTrees

The architecture is three layered: Database Storage: Snowflake has a mechanism to reorganize the data into its internal optimized, compressed and columnar format and stores this optimized data in cloud storage. The data objects are accessible only through SQL query operations run using Snowflake.

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Of Muffins and Machine Learning Models

Cloudera

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.