Flipboard

JUNE 26, 2023

In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience. Run the AWS Glue ML transform job.

AWS 123

AWS ML ML ETL 123

AWS Machine Learning Blog

DECEMBER 14, 2023

In line with this mission, Talent.com collaborated with AWS to develop a cutting-edge job recommendation engine driven by deep learning, aimed at assisting users in advancing their careers. The solution does not require porting the feature extraction code to use PySpark, as required when using AWS Glue as the ETL solution.

ETL 109

ETL AWS ML ML 109

Hacker News

MARCH 6, 2025

High-performance, low-footprint SQL database written in C++. Supports powerful features like JOIN, CDC, UPSERT, and LOOKUP, enabling real-time analytics and ETL at scale. Process millions of rows per second from Kafka, Pulsar, or ClickHouse, and seamlessly write results back.

ETL 102

ETL AWS SQL Database 102

Dataversity

MARCH 26, 2024

Writing data to an AWS data lake and retrieving it to populate an AWS RDS MS SQL database involves several AWS services and a sequence of steps for data transfer and transformation. This process leverages AWS S3 for the data lake storage, AWS Glue for ETL operations, and AWS Lambda for orchestration.

Data Lakes 64

Data Lakes SQL AWS ETL 64

Hacker News

NOVEMBER 19, 2024

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)

Computer Science 155

Computer Science Computer Science ML ML 155

Flipboard

DECEMBER 11, 2024

Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. With the SQL editor, you can query data lakes, databases, data warehouses, and federated data sources.

SQL 159

SQL AWS Data Lakes AI 159

IBM Journey to AI blog

MAY 15, 2024

Two of the more popular methods, extract, transform, load (ETL ) and extract, load, transform (ELT) , are both highly performant and scalable. ETL/ELT tools typically have two components: a design time (to design data integration jobs) and a runtime (to execute data integration jobs).

Data Pipeline 85

Data Pipeline ETL SQL Database 85

Towards AI

APRIL 3, 2024

Photo by Caspar Camille Rubin on Unsplash AWS Athena is a serverless interactive query system. So if you are familiar with the Standard SQL queries, you are good to go!! Go to the AWS Glue Console. Create a Glue Job to perform ETL operations on your data. Athena Setup Go to the AWS management console and open Athena.

AWS 105

AWS Database ETL Big Data 105

IBM Journey to AI blog

MARCH 14, 2024

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. Let’s delve into the database portfolio from IBM available on AWS. 

AWS 93

AWS Database ETL AI 93

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. Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS).

Data Engineering 195

Data Engineering Data Engineer Data Engineering 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 97

AWS Data Lakes Clustering Data Preparation 97

Pickl AI

OCTOBER 17, 2024

Summary: This article explores the significance of ETL Data in Data Management. It highlights key components of the ETL process, best practices for efficiency, and future trends like AI integration and real-time processing, ensuring organisations can leverage their data effectively for strategic decision-making.

ETL 52

ETL Data Warehouse Data Quality Data Governance 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.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

AWS Machine Learning Blog

JUNE 25, 2024

In addition, the generative business intelligence (BI) capabilities of QuickSight allow you to ask questions about customer feedback using natural language, without the need to write SQL queries or learn a BI tool. In the first step, an AWS Lambda function reads and validates the file, and extracts the raw data.

AWS 125

AWS Natural Language Processing Machine Learning Machine Learning 125

Smart Data Collective

APRIL 29, 2020

Extraction, Transform, Load (ETL). Redshift is the product for data warehousing, and Athena provides SQL data analytics. AWS Glue helps users to build data catalogues, and Quicksight provides data visualisation and dashboard construction. The services from AWS can be catered to meet the needs of each business user.

Data Warehouse 99

Data Warehouse SQL Azure ETL 99

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 131

Clustering AWS ML ML 131

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 91

AWS ML ML Data Quality 91

phData

FEBRUARY 9, 2023

Welcome to our AWS Redshift to the Snowflake Data Cloud migration blog! In this blog, we’ll walk you through the process of migrating your data from AWS Redshift to the Snowflake Data Cloud. One popular route is leveraging third-party ETL tools like Fivetran to ensure a smooth and successful migration.

AWS 59

AWS ETL Data Preparation SQL 59

Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

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

phData

SEPTEMBER 2, 2024

In this blog, we will cover the best practices for developing jobs in Matillion, an ETL/ELT tool built specifically for cloud database platforms. Matillion is a SaaS-based data integration platform that can be hosted in AWS, Azure, or GCP. Some of the supported ones for the Matillion ETL/ELT are GitHub , Bitbucket , and Azure DevOps.

ETL 52

ETL Data Warehouse SQL Database 52

AWS Machine Learning Blog

JANUARY 6, 2023

The rules in this engine were predefined and written in SQL, which aside from posing a challenge to manage, also struggled to cope with the proliferation of data from TR’s various integrated data source. TR wanted to take advantage of AWS managed services where possible to simplify operations and reduce undifferentiated heavy lifting.

AWS 86

AWS Data Warehouse ML ML 86

phData

APRIL 29, 2024

Understanding Fivetran Fivetran is a popular Software-as-a-Service platform that enables users to automate the movement of data and ETL processes across diverse sources to a target destination. For a longer overview, along with insights and best practices, please feel free to jump back to the previous blog.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

phData

AUGUST 9, 2023

This is unlike the more traditional ETL method, where data is transformed before loading into the data warehouse. By bringing raw data into the data warehouse and then transforming it there, ELT provides more flexibility compared to ETL’s fixed pipelines. ETL systems just couldn’t handle the massive flows of raw data.

ETL 52

ETL Data Warehouse Cloud Data Big Data 52

ODSC - Open Data Science

APRIL 3, 2023

Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis. Competence in data quality, databases, and ETL (Extract, Transform, Load) are essential. Cloud Services: Google Cloud Platform, AWS, Azure.

Analytics 52

Analytics Analytics Data Analyst Data Science 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. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Alation

MAY 24, 2022

SmartSuggestions — In Compose, Alation’s SQL editor, AI-powered suggestions actively show query writers relevant data to use as they query. The Lineage & Dataflow API is a good example enabling customers to add ETL transformation logic to the lineage graph. for the popular database SQL Server. Open Data Quality Initiative.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

phData

JUNE 14, 2023

In recent years, data engineering teams working with the Snowflake Data Cloud platform have embraced the continuous integration/continuous delivery (CI/CD) software development process to develop data products and manage ETL/ELT workloads more efficiently.

Data Pipeline 52

Data Pipeline Database SQL Data Engineering 52

Pickl AI

APRIL 6, 2023

In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. Hands-on experience working with SQLDW and SQL-DB. Answer : Polybase helps optimize data ingestion into PDW and supports T-SQL. Sound knowledge of relational databases or NoSQL databases like Cassandra.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

IBM Journey to AI blog

JULY 17, 2023

.” Das Kamhout, VP and Senior Principal Engineer of the Cloud and Enterprise Solutions Group at Intel Watsonx.data supports our customers’ increasing needs around hybrid cloud deployments and is available on premises and across multiple cloud providers, including IBM Cloud and Amazon Web Services (AWS).

AI 74

AI AI Machine Learning Machine Learning 74

Pickl AI

APRIL 26, 2024

It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques. Key Takeaways SQL Mastery: Understand SQL’s importance, join tables, and distinguish between SELECT and SELECT DISTINCT. How do you join tables in SQL?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

phData

FEBRUARY 14, 2023

Matillion Matillion is a complete ETL tool that integrates with an extensive list of pre-built data source connectors, loads data into cloud data environments such as Snowflake, and then performs transformations to make data consumable by analytics tools such as Tableau and PowerBI.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

ODSC - Open Data Science

JANUARY 18, 2024

These areas may include SQL, database design, data warehousing, distributed systems, cloud platforms (AWS, Azure, GCP), and data pipelines. ETL (Extract, Transform, Load) This is a core data engineering process for moving data from one or more sources to a destination, typically a data warehouse or data lake.

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. SQL SQL is crucial for querying and managing relational databases. They excel in scenarios requiring complex queries and transaction management.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

The MLOps Blog

OCTOBER 20, 2023

Example template for an exploratory notebook | Source: Author How to organize code in Jupyter notebook For exploratory tasks, the code to produce SQL queries, pandas data wrangling, or create plots is not important for readers. In those cases, most of the data exploration and wrangling will be done through SQL. documentation.

SQL 52

SQL Database Data Scientist Python 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. Enables users to trigger their custom transformations via SQL and dbt.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59