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Building cost effective data pipelines with Python & DuckDB

Start Data Engineering

Building efficient data pipelines with DuckDB 4.1. Use DuckDB to process data, not for multiple users to access data 4.2. Cost calculation: DuckDB + Ephemeral VMs = dirt cheap data processing 4.3. Processing data less than 100GB? Introduction 2. Project demo 3. Use DuckDB 4.4.

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Kafka to MongoDB: Building a Streamlined Data Pipeline

Analytics Vidhya

Handling and processing the streaming data is the hardest work for Data Analysis. We know that streaming data is data that is emitted at high volume […] The post Kafka to MongoDB: Building a Streamlined Data Pipeline appeared first on Analytics Vidhya.

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How to Implement a Data Pipeline Using Amazon Web Services?

Analytics Vidhya

Introduction The demand for data to feed machine learning models, data science research, and time-sensitive insights is higher than ever thus, processing the data becomes complex. To make these processes efficient, data pipelines are necessary. appeared first on Analytics Vidhya.

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Building Databricks Data Pipelines 101

Confessions of a Data Guy

Have you ever wondered at a high level what it’s like to build production-level data pipelines on Databricks? The post Building Databricks Data Pipelines 101 appeared first on Confessions of a Data Guy. What does it look like, what tools do you use?

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Entity Resolution: Your Guide to Deciding Whether to Build It or Buy It

Adding high-quality entity resolution capabilities to enterprise applications, services, data fabrics or data pipelines can be daunting and expensive. Organizations often invest millions of dollars and years of effort to achieve subpar results.

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Top 10 Data Pipeline Interview Questions to Read in 2023

Analytics Vidhya

Introduction Data pipelines play a critical role in the processing and management of data in modern organizations. A well-designed data pipeline can help organizations extract valuable insights from their data, automate tedious manual processes, and ensure the accuracy of data processing.

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Data Pipeline Design Patterns - #1. Data flow patterns

Start Data Engineering

Data pipeline patterns 3.1. Multi-hop pipelines 3.3.2. Conditional/ Dynamic pipelines 3.3.3. Disconnected data pipelines 4. Source Ordering 2.3. Sink Overwritability 3. Extraction patterns 3.1.1. Time ranged 3.1.2. Full Snapshot 3.1.3. Lookback 3.1.4. Streaming 3.2. Behavioral 3.2.1. Idempotent 3.2.2.