Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. Amazon’s AWS Glue is one such tool that allows you to consume data from Apache Kafka and Amazon-managed streaming for Apache Kafka (MSK).

Apache Kafka 130

Apache Kafka ETL Data Lakes AWS 130

AWS Machine Learning Blog

SEPTEMBER 18, 2024

In addition to its groundbreaking AI innovations, Zeta Global has harnessed Amazon Elastic Container Service (Amazon ECS) with AWS Fargate to deploy a multitude of smaller models efficiently. Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks.

AWS 128

AWS Machine Learning Machine Learning ML 128

Data Science Dojo

JULY 6, 2023

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications. It supports various data types and offers advanced features like data sharing and multi-cluster warehouses.

Data Engineering 195

Data Engineering Data Engineering Data Engineer Data Engineering 195

AWS Machine Learning Blog

AUGUST 21, 2023

Data is frequently kept in data lakes that can be managed by AWS Lake Formation , giving you the ability to implement fine-grained access control using a straightforward grant or revoke procedure. Account A is the data lake account that houses all the ML-ready data obtained through extract, transform, and load (ETL) processes.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

AWS Machine Learning Blog

JANUARY 17, 2024

We then discuss the various use cases and explore how you can use AWS services to clean the data, how machine learning (ML) can aid in this effort, and how you can make ethical use of the data in generating visuals and insights. For more information, refer to Common techniques to detect PHI and PII data using AWS Services.

Clustering 137

Clustering AWS ML ML 137

Flipboard

NOVEMBER 24, 2023

In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

Database 156

Database AWS ETL SQL 156

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.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL.

AWS 99

AWS ML ML Data Quality 99

AWS Machine Learning Blog

JULY 6, 2023

A number of AWS independent software vendor (ISV) partners have already built integrations for users of their software as a service (SaaS) platforms to utilize SageMaker and its various features, including training, deployment, and the model registry. In some cases, an ISV may deploy their software in the customer AWS account.

ML 106

ML ML AWS Data Scientist 106

AWS Machine Learning Blog

APRIL 16, 2024

IAM role – SageMaker requires an AWS Identity and Access Management (IAM) role to be assigned to a SageMaker Studio domain or user profile to manage permissions effectively. Create database connections The built-in SQL browsing and execution capabilities of SageMaker Studio are enhanced by AWS Glue connections. or later image versions.

SQL 130

SQL AWS Database Data Scientist 130

Women in Big Data

NOVEMBER 27, 2024

Decide between cloud-based solutions, such as AWS Redshift or Google BigQuery, and on-premises options, while considering scalability and whether a hybrid approach might be beneficial. Evaluate integration capabilities with existing data sources and Extract Transform and Load (ETL) tools.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

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.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

AWS Machine Learning Blog

JANUARY 20, 2023

In this post, we discuss how CCC Intelligent Solutions (CCC) combined Amazon SageMaker with other AWS services to create a custom solution capable of hosting the types of complex artificial intelligence (AI) models envisioned. Step-by-step solution Step 1 A client makes a request to the AWS API Gateway endpoint.

AWS 90

AWS AI AI Computer Science 90

phData

FEBRUARY 21, 2025

Examples include: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Horizontal scaling increases the quantity of computational resources dedicated to a workload; the equivalent of adding more servers or clusters. Certain CSPs are even equipped to automatically scale compute resources, based on demand.

Data Warehouse 52

Data Warehouse Cloud Data ETL Cloud Computing 52

Mlearning.ai

APRIL 24, 2023

But, it does not give you all the information about the different functionalities and services, like Data Factory/Linked Services/Analytics Synapse(how to combine and manage databases, ETL), Cognitive Services/Form Recognizer/ (how to do image, text, audio processing), IoT, Deployment, GitHub Actions (running Azure scripts from GitHub).

Azure 52

Azure SQL Database Python 52

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. These models may include regression, classification, clustering, and more. ETL Tools: Apache NiFi, Talend, etc. Cloud Platforms: AWS, Azure, Google Cloud, etc. Read more to know.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity. Apache Hadoop Hadoop is a powerful framework that enables distributed storage and processing of large data sets across clusters of computers.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

DagsHub

NOVEMBER 11, 2024

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. The features extracted in the ETL process would then be inputted into the ML models.

AI 52

AI AI Data Lakes Database 52

The MLOps Blog

MARCH 15, 2023

Some of the popular cloud-based vendors are: Hevo Data Equalum AWS DMS On the other hand, there are vendors offering on-premise data pipeline solutions and are mostly preferred by organizations dealing with highly sensitive data. Server update locks the entire cluster. User-friendly interface with live dashboards and debugging.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

DagsHub

APRIL 7, 2024

Flexibility: Its use cases are wider than just machine learning; for example, we can use it to set up ETL pipelines. Cloud-agnostic and can run on any Kubernetes cluster. Integration: It can work alongside other workflow orchestration tools (Airflow cluster or AWS SageMaker Pipelines, etc.)

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

APRIL 26, 2024

Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. Data Warehousing and ETL Processes What is a data warehouse, and why is it important? Explain the Extract, Transform, Load (ETL) process. What approach would you take?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 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. You can use familiar AWS services for model development, generative AI, data processing, and analyticsall within a single, governed environment.

SQL 126

SQL Data Analyst Data Warehouse AWS 126

DagsHub

OCTOBER 23, 2024

Apache Hadoop Apache Hadoop is an open-source framework that supports the distributed processing of large datasets across clusters of computers. is similar to the traditional Extract, Transform, Load (ETL) process. Tooling : Apache Tika , ElasticSearch , Databricks , and AWS Glue for metadata extraction and management.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

phData

OCTOBER 28, 2024

Modern low-code/no-code ETL tools allow data engineers and analysts to build pipelines seamlessly using a drag-and-drop and configure approach with minimal coding. One such option is the availability of Python Components in Matillion ETL, which allows us to run Python code inside the Matillion instance.

Python 52

Python ETL AWS Database 52

Flipboard

DECEMBER 4, 2024

For governance, it uses AWS Glue Data Catalog as the central technical catalog and AWS Lake Formation as the permission store for enforcing fine-grained access controls. The Data Engineer has an IAM ETL role and runs the extract, transform, and load (ETL) pipeline using Spark to populate the Lakehouse catalog on RMS.

Data Lakes 138

Data Lakes Data Warehouse AWS Database 138

Flipboard

MAY 15, 2025

You can bring data from operational databases and applications into your lakehouse in near real time through zero-ETL integrations. With SageMaker Lakehouse, you can access tables stored in Amazon Redshift managed storage (RMS) through Iceberg APIs, using the Iceberg REST catalog backed by AWS Glue Data Catalog. and Amazon EMR 7.5.0

AWS 117

AWS SQL Data Lakes Data Warehouse 117