Data Ingestion Architecture - Learn Data Science with Travis

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Follow These Best Practices for High-Quality Data Ingestion

Towards Data Science

How to choose the right tool and integrate it into your data pipeline Continue reading on Towards Data Science

Data ingestion is (almost) a solved problem

Towards Data Science

Ask anyone who has been involved in a data related job over the past 10–15 years what is the most boring task they would rather avoid, and chances are many would answer ‘data ingestion’. Everyone…

What is Data Ingestion?

Towards Data Science

Do you use navigation software to get from one place to another? Did you buy a book on Amazon? Did you watch “Stranger Things” on Netflix? Did you look for a funny video on YouTube? If you answered…

Using Databricks Autoloader to support Event-Driven Data Ingestion

Towards Data Science

Simplifying incremental ingestion of data into the Lakehouse with Autoloader Continue reading on Towards Data Science

Data Engineering: Incremental Data Loading Strategies

Towards Data Science

Years of serving as a data engineer and analyst working on integrating many data sources into enterprise data platforms, I managed to encounter one complexity after another when trying to incrementall...

Real-Time Message Ingestion to Big Data Platform

Better Programming

A practice to ingest the data in real-time from Kafka cluster to the Hadoop/HDFS platform Photo by Joshua Sortino on Unsplash It is quite a common requirement to ingest the data from the microservice ...

The Reactive Streams Ingestion (RSI) Library— DataLoad Mode

Oracle Developers

High-performance data access with Java by Juarez Junior Introduction Part 1 in this series introduced the Java Library for Reactive Stream Ingestion (RSI), its API, and Oracle Database Free as the tar...

Data Management Architectures — Monolithic Data Architectures and Distributed Data Mesh

Towards Data Science

A data management architecture governs how organizations collect, store, secure, arrange, integrate and use data. A good data management architecture provides clarity about every aspect of data and…

An Architecture for the Data Mesh

Towards Data Science

Data is the new gold, or so they say. But recent efforts to mine the value of this data have far too often failed. And in some cases, failed dismally. We tried data warehouses, but inconsistent data…

The Data Mesh architecture

Towards Data Science

The architecture of data is not just a technical architecture but is also an organizational structure, therefore, making it a key factor for building any data empire. Over time there have been…

What is the Data Architecture we Need?

Towards Data Science

In the new era of Big Data and Data Sciences, it is vitally important for an enterprise to have a centralized data architecture aligned with business processes, which scales with business growth and…

What makes Amazon Kinesis Data Streams useful for streaming ?

Analytics Vidhya

Following are some of those scenarios that often occurs within various operations (Back office, IT etc..). You are most likely looking for a data ingestion solution that deals with streaming data…

System Design Series: 0 to 100 Guide to Data Streaming Systems

Towards Data Science

System Design Series: The Ultimate Guide for Building High-Performance Data Streaming Systems from Scratch! Source: Unsplash Setting up an example problem: A Recommendationxt System “Data Streaming” ...

A High-Speed Data Ingestion Microservice in Java Using MQTT, AMQP, and STOMP

Oracle Developers

by Juarez JuniorIn a couple of previous blog posts, I’ve introduced Java developers to the Reactive Streams Ingestion (RSI) library.Part 1 introduced the Java Library for Reactive Stream Ingestion (RS...

Data pipelines in a nutshell

Python in Plain English

Just as water originates in lakes, oceans, and rivers, data begins in data lakes, databases, and through real-time streaming. However, both raw water and raw data are unfit for direct consumption or u...

Managing your data flows with Apache Nifi

Analytics Vidhya

Data flow is a recurrent use case faced by many companies and institutions. There is always the need to handle an incoming source of data, transform it somehow, and send it to another system. These…

Hadoop Distributed File System (HDFS) Architecture — A Guide to HDFS for Every Data Engineer

Analytics Vidhya

In contemporary times, it is commonplace to deal with massive amounts of data. From your next WhatsApp message to your next Tweet, you are creating data at every step when you interact with…

Data Pipeline Design Principles

Towards Data Science

In 2020, the field of open-source Data Engineering is finally coming-of-age. In addition to the heavy duty proprietary software for creating data pipelines, workflow orchestration and testing, more…

Advanced ETL Techniques for Beginners

Towards Data Science

Data ingestion is a crucial step in data engineering. Data engineers load huge amounts of data into various database systems for further transformation and processing. While dealing with relatively sm...

Data Integration — Things to Consider

Towards Data Science

When integrating data from system A to system B, data engineers and other stakeholders should not only focus on the data process, e.g. via ETL/ELT, but also on the source system. What various…

Change Data Capture (CDC) for Data Lake Data Ingestion

Towards Data Science

Change Data Capture(CDC) tools can accelerate Data Lake adoption by enabling scalable and network efficient near-real-time data replication to Data Lakes

Data Ingestion from 5 Major Data Sources using Python

Towards AI

Learn how to ingest data from 5 Major data sources using python. These data sources are RDBMS database, CSV, Parquet, XML, and CSV.

Big Data Architecture Concepts

Analytics Vidhya

With the advancement of technology, the volumes of data organisation’s collect have increased exponentially. A big data architecture is used to ingest, process and analyse data that is too…

Data Pipeline Engineering Towards High Data Availability

Towards Data Science

Extracting insights and making predictions from data are my primary goals, however, before I can get value from data, I first need to acquire data from data warehouse. Usually the data I need is not…