AWS Machine Learning Blog

FEBRUARY 2, 2024

Because embeddings are an important source of data for NLP models in general and generative AI solutions in particular, we need a way to measure whether our embeddings are changing over time (drifting). Then we use K-Means to identify a set of cluster centers.

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AWS Clustering ETL Database 132

Pickl AI

AUGUST 9, 2024

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of Big Data Understanding the fundamentals of Big Data is crucial for anyone entering this field.

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Big Data Big Data Big Data Analytics Big Data Analytics 52

Flipboard

NOVEMBER 27, 2024

Enterprises are facing challenges in accessing their data assets scattered across various sources because of increasing complexities in managing vast amount of data. Traditional search methods often fail to provide comprehensive and contextual results, particularly for unstructured data or complex queries.

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Pickl AI

JANUARY 27, 2025

Summary: HDFS in Big Data uses distributed storage and replication to manage massive datasets efficiently. By co-locating data and computations, HDFS delivers high throughput, enabling advanced analytics and driving data-driven insights across various industries. It fosters reliability. between 2024 and 2030.

Hadoop 52

Hadoop Big Data Big Data Clustering 52

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

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Flipboard

NOVEMBER 24, 2023

The SnapLogic Intelligent Integration Platform (IIP) enables organizations to realize enterprise-wide automation by connecting their entire ecosystem of applications, databases, big data, machines and devices, APIs, and more with pre-built, intelligent connectors called Snaps.

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Database AWS ETL SQL 158

phData

OCTOBER 9, 2023

In this blog, we explore best practices and techniques to optimize Snowflake’s performance for data vault modeling , enabling your organizations to achieve efficient data processing, accelerated query performance, and streamlined ETL workflows. However, joining tables using a hash key can take longer than a sequential key.

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AWS Machine Learning Blog

AUGUST 21, 2023

Our customers wanted the ability to connect to Amazon EMR to run ad hoc SQL queries on Hive or Presto to query data in the internal metastore or external metastore (such as the AWS Glue Data Catalog ), and prepare data within a few clicks. The outputs of this template are as follows: An S3 bucket for the data lake.

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AWS Data Lakes Clustering Data Preparation 98

Pickl AI

SEPTEMBER 19, 2024

It discusses performance, use cases, and cost, helping you choose the best framework for your big data needs. Introduction Apache Spark and Hadoop are potent frameworks for big data processing and distributed computing. While both handle vast datasets across clusters, they differ in approach. What is Apache Spark?

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Hadoop Big Data Big Data Clustering 52

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

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Data Science Machine Learning Machine Learning Data Visualization 150

ODSC - Open Data Science

SEPTEMBER 29, 2023

But, the amount of data companies must manage is growing at a staggering rate. Research analyst firm Statista forecasts global data creation will hit 180 zettabytes by 2025. In our discussion, we cover the genesis of the HPCC Systems data lake platform and what makes it different from other big data solutions currently available.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

Mlearning.ai

MAY 16, 2023

There are various architectural design patterns in data engineering that are used to solve different data-related problems. This article discusses five commonly used architectural design patterns in data engineering and their use cases. Finally, the transformed data is loaded into the target system.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

JULY 6, 2023

· Hive Execution Engine It executes the generated query plans on the Hadoop cluster. Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. This compilation process optimizes the query plan to leverage parallel processing and minimize data movement.

Hadoop 52

Hadoop SQL Big Data Big Data 52

Pickl AI

JULY 30, 2024

Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation. With a user-friendly interface and robust features, NiFi simplifies complex data workflows and enhances real-time data integration. Its visual interface allows users to design complex ETL workflows with ease.

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ETL Data Lakes Big Data Big Data 52

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks. He is passionate about machine learning engineering, distributed systems, and big-data technologies. Every Airflow task calls Amazon ECS tasks with some overrides.

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AWS Machine Learning Machine Learning ML 128

Pickl AI

JANUARY 28, 2025

Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format. Data Quality Management : Ensures that the integrated data is accurate, consistent, and reliable for analysis. They store structured data in a format that facilitates easy access and analysis.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

phData

APRIL 20, 2023

Unlike traditional data warehousing solutions, Snowflake brings critical features like Data Sharing , Snowpipe, Streams, and Time-Travel to the enterprise data architecture space. What is Data Vault Modeling? By combining the Snowflake Data Cloud with a Data Vault 2.0

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Data Warehouse Data Governance Clustering Database 72

DagsHub

NOVEMBER 11, 2024

Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data. These capture the semantic relationships between words, facilitating tasks like classification and clustering within ETL pipelines. Multimodal embeddings help combine unstructured data from various sources in data warehouses and ETL pipelines.

AI 52

AI AI Data Lakes Database 52

Pickl AI

NOVEMBER 4, 2024

Introduction Data Engineering is the backbone of the data-driven world, transforming raw data into actionable insights. As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

The MLOps Blog

MARCH 15, 2023

Talend Overview While Talend’s Open Studio for Data Integration is free-to-download software to start a basic data integration or an ETL project, it also comes powered with more advanced features which come with a price tag. Server update locks the entire cluster. It supports multiple file formats.

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Data Pipeline ETL SQL Data Quality 59

Pickl AI

APRIL 26, 2024

I would perform exploratory data analysis to understand the distribution of customer transactions and identify potential segments. Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. What approach would you take?

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Data Analyst Data Analysis Data Analysis Machine Learning 52

DagsHub

OCTOBER 23, 2024

Data Lakes Data lakes are centralized repositories designed to store vast amounts of raw, unstructured, and structured data in their native format. They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications. Unstructured.io

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Machine Learning Machine Learning Data Lakes AI 52

Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

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SQL Data Analyst Data Warehouse AWS 141

Flipboard

DECEMBER 4, 2024

The Data Lake Admin has an AWS Identity and Access Management (IAM) admin role and is a Lake Formation administrator responsible for managing user permissions to catalog objects using Lake Formation. The Data Warehouse Admin has an IAM admin role and manages databases in Amazon Redshift.

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Data Lakes Data Warehouse AWS Database 139

Flipboard

MAY 15, 2025

It secures your data in the lakehouse by defining fine-grained permissions, which are consistently applied across all analytics and ML tools and engines. You can bring data from operational databases and applications into your lakehouse in near real time through zero-ETL integrations. config(f'spark.sql.catalog.

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AWS SQL Data Lakes Data Warehouse 138