AWS Machine Learning Blog

FEBRUARY 21, 2025

Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. Responsibility for maintenance and troubleshooting: Rockets DevOps/Technology team was responsible for all upgrades, scaling, and troubleshooting of the Hadoop cluster, which was installed on bare EC2 instances.

Data Science 81

Data Science AWS Hadoop Data Scientist 81

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. At the heart of this process lie ETL Tools—Extract, Transform, Load—a trio that extracts data, tweaks it, and loads it into a destination. Choosing the right ETL tool is crucial for smooth data management. What is ETL?

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ETL Data Quality Data Pipeline Data Warehouse 40

Flipboard

NOVEMBER 24, 2023

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning. Room for improvement!

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Database AWS ETL SQL 158

AWS Machine Learning Blog

JANUARY 17, 2024

To obtain such insights, the incoming raw data goes through an extract, transform, and load (ETL) process to identify activities or engagements from the continuous stream of device location pings. We can analyze activities by identifying stops made by the user or mobile device by clustering pings using ML models in Amazon SageMaker.

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Clustering AWS ML ML 134

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. This can make it nearly impossible to “handwrite” these SQL queries.

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ETL Clustering Data Warehouse SQL 69

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

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Data Science Machine Learning Machine Learning Data Visualization 150

IBM Journey to AI blog

SEPTEMBER 25, 2023

This blog takes you on a journey into the world of Uber’s analytics and the critical role that Presto, the open source SQL query engine, plays in driving their success. This allowed them to focus on SQL-based query optimization to the nth degree. They also put process automation in place to quickly set up and take down clusters.

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Data Lakes Analytics Analytics Clustering 102

Women in Big Data

NOVEMBER 27, 2024

Evaluate integration capabilities with existing data sources and Extract Transform and Load (ETL) tools. Its PostgreSQL foundation ensures compatibility with most SQL clients. Strengths : Real-time analytics, built-in machine learning capabilities, and fast querying with standard SQL.

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Data Warehouse Big Data Big Data Azure 59

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. An EMR cluster with EMR runtime roles enabled. internal in the certificate subject definition.

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Applied Data Science

AUGUST 2, 2021

They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data.

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Data Science Data Scientist ML ML 130

Pickl AI

SEPTEMBER 19, 2024

While both handle vast datasets across clusters, they differ in approach. It distributes large datasets across multiple nodes in a cluster , ensuring data availability and fault tolerance. Data is processed in parallel across the cluster in the map phase, while in the Reduce phase, the results are aggregated.

Hadoop 52

Hadoop Big Data Big Data Clustering 52

Pickl AI

JULY 6, 2023

It has the following features: It facilitates querying, summarizing, and analyzing large datasets Hadoop also provides a SQL-like language called HiveQL Hive allows users to write queries to extract valuable insights from structured and semi-structured data stored in Hadoop. Hive is a data warehousing infrastructure built on top of Hadoop.

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Hadoop SQL Big Data Big Data 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 Engineering Data Engineer Data Engineering 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).

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Azure SQL Database Python 52

AWS Machine Learning Blog

OCTOBER 9, 2024

Using Amazon Redshift ML for anomaly detection Amazon Redshift ML makes it easy to create, train, and apply machine learning models using familiar SQL commands in Amazon Redshift data warehouses. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL. Choose Delete.

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AWS ML ML Data Quality 95

ODSC - Open Data Science

SEPTEMBER 29, 2023

Spark is more focused on data science, ingestion, and ETL, while HPCC Systems focuses on ETL and data delivery and governance. It’s not a widely known programming language like Java, Python, or SQL. ECL sounds compelling, but it is a new programming language and has fewer users than languages like Python or SQL.

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Data Lakes Clustering Big Data Big Data 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. Every Airflow task calls Amazon ECS tasks with some overrides. Additionally, we’re using a custom Airflow operator called ECSTaskLogOperator that allows us to process Amazon CloudWatch logs using downstream systems.

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AWS Machine Learning Machine Learning ML 122

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?

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Data Analyst Data Analysis Data Analysis Machine Learning 52

phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. DataFrames are able to be created from tables, views, streams, and stages, from the results of a SQL query, or from hardcoded values. What is Snowpark? filter(col("id") == 1).select(col("name"),

Python 52

Python ML ML SQL 52

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. What Are Some Common Tools Used in Business Intelligence Architecture?

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

The MLOps Blog

MARCH 15, 2023

Enables users to trigger their custom transformations via SQL and dbt. 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.

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Data Pipeline ETL SQL Data Quality 59

phData

AUGUST 10, 2023

That said, dbt provides the ability to generate data vault models and also allows you to write your data transformations using SQL and code-reusable macros powered by Jinja2 to run your data pipelines in a clean and efficient way. Macros can be called in models and then generated into additional SQL snippets or even the entire SQL code.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

phData

APRIL 20, 2023

To set up this approach, a multi-cluster warehouse is recommended for stage loads, and separate multi-cluster warehouses can be used to run all loads in parallel. If data is present, Tasks runs SQL to push it to the raw data vault objects. The stream shows the ‘delta’ that needs processing.

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Data Warehouse Data Governance Clustering Database 72

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 Engineering Data Engineer Data Engineering 52

phData

FEBRUARY 5, 2024

Setting up the Information Architecture Setting up an information architecture during migration to Snowflake poses challenges due to the need to align existing data structures, types, and sources with Snowflake’s multi-cluster, multi-tier architecture. Essentially, it functions like Google Translate — but for SQL dialects.

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SQL Database Data Quality Data Warehouse 52

Pickl AI

JANUARY 27, 2025

Definition of HDFS HDFS is an open-source file system that manages files across a cluster of commodity servers. NameNode The NameNode is your HDFS cluster’s central authority, maintaining the file systems directory tree and metadata. You can seamlessly add new Data Nodes to the Hadoop cluster without disrupting ongoing tasks.

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Hadoop Big Data Big Data Clustering 52

Dataconomy

JULY 28, 2023

Techniques like binning, regression, and clustering are employed to smooth and filter the data, reducing noise and improving the overall quality of the dataset. Toad Data Point Toad Data Point is a user-friendly tool that makes querying and updating data with SQL simple and efficient.

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Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. Understanding the differences between SQL and NoSQL databases is crucial for students.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

DagsHub

APRIL 7, 2024

Thanks to its various operators, it is integrated with Python, Spark, Bash, SQL, and more. 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. via Skypilot or another orchestrator defined in your MLOps stack).

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Machine Learning Machine Learning ML ML 59

ODSC - Open Data Science

DECEMBER 9, 2024

SciKit-Learn : A popular machine learning library with consistent APIs for regression, classification, clustering, dimensionality reduction, and model selection techniques. Switching contexts across tools like Pandas, SciKit-Learn, SQL databases, and visualization engines creates cognitive burden.

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Data Science Machine Learning Machine Learning Python 40

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

DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. Apache Hadoop Apache Hadoop is an open-source framework that supports the distributed processing of large datasets across clusters of computers. Unstructured.io

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.

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Python ETL AWS Database 52

Flipboard

DECEMBER 4, 2024

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. Using Amazon Redshift Sign in to the Redshift Sale cluster QEV2 using the IAM Analyst role. 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