Remove Aggregated Data Remove Data Collection Remove Data Ingestion
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What is a Data Pipeline (and 7 Must-Have Features of Modern Data Pipelines)

Striim

Whether you’re in the healthcare industry or logistics, being data-driven is equally important. Here’s an example: Suppose your fleet management business uses batch processing to analyze vehicle data. This interconnected approach enables teams to create, manage, and automate data pipelines with ease and minimal intervention.

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Predictive Analytics in Logistics: Forecasting Demand and Managing Risks

Striim

In contrast, data streaming offers continuous, real-time integration and analysis, ensuring predictive models always use the latest information. Data transformation includes normalizing data, encoding categorical variables, and aggregating data at the appropriate granularity. Here’s the process.

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Case Study: How Rockset's Real-Time Analytics Platform Propels the Growth of Our NFT Marketplace

Rockset

One was to create another data pipeline that would aggregate data as it was ingested into DynamoDB. And with the NFL season set to start in less than a month, we were in a bind. A Faster, Friendlier Solution We considered a few alternatives. Another was to scrap DynamoDB and find a traditional SQL database.

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Data Warehousing Guide: Fundamentals & Key Concepts

Monte Carlo

This article will define in simple terms what a data warehouse is, how it’s different from a database, fundamentals of how they work, and an overview of today’s most popular data warehouses. What is a data warehouse? Yes, data warehouses can store unstructured data as a blob datatype. They need to be transformed.

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Tips to Build a Robust Data Lake Infrastructure

DareData

Users: Who are users that will interact with your data and what's their technical proficiency? Data Sources: How different are your data sources? Latency: What is the minimum expected latency between data collection and analytics? And what is their format?

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. integration) and preprocessing need to run at scale.

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Build Internal Apps in Minutes with Retool and Rockset: A Customer 360 Example

Rockset

Essentially, Rockset is an indexing layer on top of DynamoDB and Amazon Kinesis, where we can join, search, and aggregate data from these sources. From there, we’ll create a data API for the SQL query we write in Rockset. When an associate converses with the customer, they can handle the customer’s situation appropriately.