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

Analytics Vidhya

DECEMBER 4, 2020

Overview Know which are the top 9 skills required to be a data engineer Find suitable resources to learn about these tools By no. The post 9 Must-Have Skills to Become a Data Engineer! appeared first on Analytics Vidhya.

Data Engineering 265

Data Engineering Data Engineer Data Engineering Data Engineering 265

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

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 80

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

Analytics Vidhya

SEPTEMBER 22, 2022

This article was published as a part of the Data Science Blogathon. 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. Introduction “Learning is an active process.

Data Pipeline 234

Data Pipeline Apache Kafka Internet of Things Data Science 234

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. BECOME a WRITER at MLearning.ai.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Data Science Dojo

MARCH 9, 2023

It requires minimal operational maintenance and allows for rapid development, resulting in significant cost savings and reduced development time for data-focused developers and engineers. Handling too many data sources can become overwhelming, especially with complex schemas.

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Apache Kafka Azure Data Science Data Pipeline 326

Women in Big Data

OCTOBER 9, 2024

Refer to Unlocking the Power of Big Data Article to understand the use case of these data collected from various sources. Data Ingestion: Data is collected and funneled into the pipeline using batch or real-time methods, leveraging tools like Apache Kafka, AWS Kinesis, or custom ETL scripts.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

AWS Machine Learning Blog

OCTOBER 9, 2024

With ML-powered anomaly detection, customers can find outliers in their data without the need for manual analysis, custom development, or ML domain expertise. Using Amazon Glue Data Quality for anomaly detection Data engineers and analysts can use AWS Glue Data Quality to measure and monitor their data.

AWS 90

AWS ML ML Data Quality 90

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 (..)

AI 40

AI AI Apache Kafka Machine Learning 40

DagsHub

OCTOBER 23, 2024

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Data Science Blog

JUNE 27, 2023

This architectural concept relies on event streaming as the core element of data delivery. In practical implementation, the Kappa architecture is commonly deployed using Apache Kafka or Kafka-based tools. Applications can directly read from and write to Kafka or an alternative message queue tool.

Big Data 130

Big Data Big Data Apache Kafka Database 130

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 90

ML ML Apache Kafka SQL 90

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. Activity Schema Processing : To capture and process customer activities, you might use a stream processing technology like Apache Kafka or Apache Flink.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

The MLOps Blog

FEBRUARY 5, 2023

The machine learning model is part of the Stream processing engine, and it provides the logic that helps the streaming data pipeline expose features within the stream and potentially within a historical data store. 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

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