Remove Data Ingestion Remove NoSQL Remove Structured Data
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Why Real-Time Analytics Requires Both the Flexibility of NoSQL and Strict Schemas of SQL Systems

Rockset

Traditional databases, with their wholly-inflexible structures, are brittle. So are schemaless NoSQL databases, which capably ingest firehoses of data but are poor at extracting complex insights from that data. And the same risk of data errors and data downtime also exists. NoSQL Comes to the Rescue.

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Smart Schema: Enabling SQL Queries on Semi-Structured Data

Rockset

In this blog post, we show how Rockset’s Smart Schema feature lets developers use real-time SQL queries to extract meaningful insights from raw semi-structured data ingested without a predefined schema. This is particularly true given the nature of real-world data.

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Data Warehouse vs Big Data

Knowledge Hut

Data warehouses are typically built using traditional relational database systems, employing techniques like Extract, Transform, Load (ETL) to integrate and organize data. Data warehousing offers several advantages. By structuring data in a predefined schema, data warehouses ensure data consistency and accuracy.

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Most important Data Engineering Concepts and Tools for Data Scientists

DareData

Our goal is to help data scientists better manage their models deployments or work more effectively with their data engineering counterparts, ensuring their models are deployed and maintained in a robust and reliable way. DigDag: An open-source orchestrator for data engineering workflows.

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

Striim

Data Collection/Ingestion The next component in the data pipeline is the ingestion layer, which is responsible for collecting and bringing data into the pipeline. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis.

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Data Collection for Machine Learning: Steps, Methods, and Best Practices

AltexSoft

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Key differences between structured, semi-structured, and unstructured data.

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Big Data Analytics: How It Works, Tools, and Real-Life Applications

AltexSoft

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. Big Data analytics processes and tools.