Pickl AI

NOVEMBER 25, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

It initially sources input time series data from Amazon Managed Streaming for Apache Kafka (Amazon MSK) using this live stream for model training. Post-training, the model continues to process incoming data points from the stream. It evaluates these points against the historical trends of the corresponding time series.

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ODSC - Open Data Science

JULY 22, 2023

Leverage Compound Sparsity to Achieve the Fastest Inference Performance on CPUs: Damian Bogunowicz | Neural Magic and Konstantin Gulin | Machine Learning Engineer | Neural Magic Apache Kafka for Real-Time Machine Learning Without a Data Lake: Kai Waehner | Global Field CTO | Author, International Speaker Time Series Forecasting for Managers — All Forecasts (..)

Apache Kafka 52

Apache Kafka Machine Learning Machine Learning Data Science 52

Pickl AI

SEPTEMBER 18, 2024

Read More: How Airbnb Uses Big Data and Machine Learning to Offer World-Class Service Netflix’s Big Data Infrastructure Netflix’s data infrastructure is one of the most sophisticated globally, built primarily on cloud technology. petabytes of data. What Technologies Does Netflix Use for Its Big Data Infrastructure?

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Big Data Big Data Apache Kafka Big Data Analytics 52

ODSC - Open Data Science

JULY 14, 2023

The session participants will learn the theory behind compound sparsity, state-of-the-art techniques, and how to apply it in practice using the Neural Magic platform.

Machine Learning 52

Machine Learning Machine Learning Apache Kafka Data Science 52

Pickl AI

JULY 30, 2024

Organizations can monitor the lineage of data as it moves through the system, providing visibility into data transformations and ensuring compliance with data governance policies. Integration with Big Data Ecosystems NiFi integrates seamlessly with Big Data technologies such as Apache Hadoop, Apache Kafka, and Apache Spark.

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ETL Data Lakes Big Data Big Data 52

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

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

AWS Machine Learning Blog

MARCH 30, 2023

For every xSaves prediction, it produces a message with the prediction as a payload, which then gets distributed by a central message broker running on Amazon Managed Streaming for Apache Kafka (Amazon MSK). The information also gets stored in a data lake for future auditing and model improvements.

Machine Learning 81

Machine Learning Machine Learning AWS Apache Kafka 81

ODSC - Open Data Science

OCTOBER 9, 2024

What should you be looking for?

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Apache Kafka AI AI Machine Learning 40

Pickl AI

AUGUST 9, 2024

APIs Understanding how to interact with Application Programming Interfaces (APIs) to gather data from external sources. Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Dataconomy

FEBRUARY 23, 2023

Data engineers are responsible for designing and building the systems that make it possible to store, process, and analyze large amounts of data. These systems include data pipelines, data warehouses, and data lakes, among others. However, building and maintaining these systems is not an easy task.

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Big Data Big Data Data Engineer Data Engineering 91

Pickl AI

JULY 8, 2024

Common options include: Relational Databases: Structured storage supporting ACID transactions, suitable for structured data. NoSQL Databases: Flexible, scalable solutions for unstructured or semi-structured data. Data Warehouses : Centralised repositories optimised for analytics and reporting.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

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. Both persistent staging and data lakes involve storing large amounts of raw data. These changes are streamed into Iceberg tables in your data lake.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Alation

AUGUST 4, 2022

And where data was available, the ability to access and interpret it proved problematic. Big data can grow too big fast. Left unchecked, data lakes became data swamps. Some data lake implementations required expensive ‘cleansing pumps’ to make them navigable again.

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Big Data Big Data Apache Kafka Data Lakes 95