Analytics Vidhya

SEPTEMBER 22, 2022

Dale Carnegie” Apache Kafka is a Software Framework for storing, reading, and analyzing streaming data. The post Build a Simple Realtime Data Pipeline appeared first on Analytics Vidhya. Only knowledge that is used sticks in your mind.- The Internet of Things(IoT) devices can generate a large […].

Data Pipeline 270

Data Pipeline Apache Kafka Internet of Things Data Science 270

Data Science Dojo

JULY 24, 2023

They allow data processing tasks to be distributed across multiple machines, enabling parallel processing and scalability. It involves various technologies and techniques that enable efficient data processing and retrieval. Stay tuned for an insightful exploration into the world of Big Data Engineering with Distributed Systems!

Big Data 195

Big Data Big Data Data Engineering Data Engineer 195

IBM Journey to AI blog

NOVEMBER 3, 2023

It allows your business to ingest continuous data streams as they happen and bring them to the forefront for analysis, enabling you to keep up with constant changes. Apache Kafka boasts many strong capabilities, such as delivering a high throughput and maintaining a high fault tolerance in the case of application failure.

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Apache Kafka Data Warehouse Data Pipeline Big Data 79

Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. What is data engineering?

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

ODSC - Open Data Science

FEBRUARY 6, 2024

Data engineering has become an integral part of the modern tech landscape, driving advancements and efficiencies across industries. So let’s explore the world of open-source tools for data engineers, shedding light on how these resources are shaping the future of data handling, processing, and visualization.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Data Science Blog

JUNE 27, 2023

Big Data Analytics stands apart from conventional data processing in its fundamental nature. In the realm of Big Data, there are two prominent architectural concepts that perplex companies embarking on the construction or restructuring of their Big Data platform: Lambda architecture or Kappa architecture.

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Big Data Big Data Apache Kafka Database 130

Women in Big Data

OCTOBER 9, 2024

Once ingested, the data is prepared through filtering, error correction, and restructuring for ease of use. After this, the data is analyzed, business logic is applied, and it is processed for further analytical tasks like visualization or machine learning.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

AWS Machine Learning Blog

OCTOBER 9, 2024

Using Amazon Redshift ML for anomaly detection Amazon Redshift ML makes it easy to create, train, and apply machine learning models using familiar SQL commands in Amazon Redshift data warehouses. Using Amazon Glue Data Quality for anomaly detection Data engineers and analysts can use AWS Glue Data Quality to measure and monitor their data.

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AWS ML ML Data Quality 90

The MLOps Blog

FEBRUARY 5, 2023

A streaming data pipeline is an enhanced version which is able to handle millions of events in real-time at scale. With that capability, applications, analytics, and reporting can be done in real-time. It can be used to collect, store, and process streaming data in real-time.

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

ODSC - Open Data Science

OCTOBER 9, 2024

Efficient Incremental Processing with Apache Iceberg and Netflix Maestro Dimensional Data Modeling in the Modern Era Building Big Data Workflows: NiFi, Hive, Trino, & Zeppelin An Introduction to Data Contracts From Data Mess to Data Mesh — Data Management in the Age of Big Data and Gen AI Introduction to Containers for Data Science / Data Engineering (..)

Apache Kafka 40

Apache Kafka AI AI Machine Learning 40

phData

SEPTEMBER 27, 2024

Technologies like Apache Kafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time. It enables advanced analytics, makes debugging your marketing automations easier, provides natural audit trails for compliance, and allows for flexible, evolving customer data models.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

AWS Machine Learning Blog

APRIL 19, 2023

Streaming ingestion – An Amazon Kinesis Data Analytics for Apache Flink application backed by Apache Kafka topics in Amazon Managed Streaming for Apache Kafka (MSK) (Amazon MSK) calculates aggregated features from a transaction stream, and an AWS Lambda function updates the online feature store.

ML 95

ML ML Apache Kafka SQL 95

The MLOps Blog

AUGUST 11, 2023

1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g., 1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g.,

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines. Thats where data engineering tools come in!

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

MARCH 13, 2025

Two of the most popular message brokers are RabbitMQ and Apache Kafka. In this blog, we will explore RabbitMQ vs Kafka, their key differences, and when to use each. Kafka excels in real-time data streaming and scalability. RabbitMQ uses a push-based model, while Kafka follows a pull-based model.

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Apache Kafka Big Data Big Data Data Pipeline 52