IBM Journey to AI blog

MARCH 14, 2024

The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage. Also, traditional database management tasks, including backups, upgrades and routine maintenance drain valuable time and resources, hindering innovation.

AWS 93

AWS Database ETL AI 93

AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

Data Warehouse 116

Data Warehouse Machine Learning Machine Learning Cloud Data 116

phData

NOVEMBER 4, 2024

With data software pushing the boundaries of what’s possible in order to answer business questions and alleviate operational bottlenecks, data-driven companies are curious how they can go “beyond the dashboard” to find the answers they are looking for. One of the standout features of Dataiku is its focus on collaboration.

Machine Learning 69

Machine Learning Machine Learning Data Science ML 69

Pickl AI

APRIL 10, 2023

Data Analysis is one of the most crucial tasks for business organisations today. SQL or Structured Query Language has a significant role to play in conducting practical Data Analysis. That’s where SQL comes in, enabling data analysts to extract, manipulate and analyse data from multiple sources.

Data Analyst 52

Data Analyst SQL Data Analysis Data Analysis 52

How to Learn Machine Learning

APRIL 26, 2025

Data Sources and Collection Everything in data science begins with data. Data can be generated from databases, sensors, social media platforms, APIs, logs, and web scraping. Data can be in structured (like tables in databases), semi-structured (like XML or JSON), or unstructured (like text, audio, and images) form.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

Data Science Dojo

JANUARY 12, 2023

Data is loaded into the Hadoop Distributed File System (HDFS) and stored on the many computer nodes of a Hadoop cluster in deployments based on the distributed processing architecture. However, instead of using Hadoop, data lakes are increasingly being constructed using cloud object storage services.

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Data Lakes Data Warehouse Hadoop Machine Learning 221

Tableau

JULY 28, 2020

Ryan Cairnes Senior Manager, Product Management, Tableau Hannah Kuffner July 28, 2020 - 10:43pm March 20, 2023 Tableau Prep is a citizen data preparation tool that brings analytics to anyone, anywhere. With Prep, users can easily and quickly combine, shape, and clean data for analysis with just a few clicks. billion records!

Data Preparation 52

Data Preparation Tableau Database Clean Data 52

Tableau

JULY 28, 2020

Ryan Cairnes Senior Manager, Product Management, Tableau Hannah Kuffner July 28, 2020 - 10:43pm March 20, 2023 Tableau Prep is a citizen data preparation tool that brings analytics to anyone, anywhere. With Prep, users can easily and quickly combine, shape, and clean data for analysis with just a few clicks. billion records!

Data Preparation 52

Data Preparation Tableau Database Clean Data 52

AWS Machine Learning Blog

NOVEMBER 27, 2023

Data preparation is important at multiple stages in Retrieval Augmented Generation ( RAG ) models. Below, we show how you can do all these main preprocessing steps from Amazon SageMaker Data Wrangler : Extracting text from a PDF document (powered by Textract) Remove sensitive information (powered by Comprehend) Chunk text into pieces.

Data Preparation 115

Data Preparation AI AI Python 115

ODSC - Open Data Science

SEPTEMBER 25, 2023

However, the majority of enterprise data remains unleveraged from an analytics and machine learning perspective, and much of the most valuable information remains in relational database schemas such as OLAP. Data preparation happens at the entity-level first so errors and anomalies don’t make their way into the aggregated dataset.

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Data Preparation Machine Learning Machine Learning ML 104

AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

ML 98

ML ML Database AWS 98

Becoming Human

MAY 15, 2023

One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases. Each library has its own functionality and depth so, learning these libraries with any data (data frame) makes your learning go in the right direction. Why do we need databases?

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. You can use query_string to filter your dataset by SQL and unload it to Amazon S3.

ML 123

ML ML AWS Data Warehouse 123

ODSC - Open Data Science

JUNE 12, 2023

They all agree that a Datamart is a subject-oriented subset of a data warehouse focusing on a particular business unit, department, subject area, or business functionality. The Datamart’s data is usually stored in databases containing a moving frame required for data analysis, not the full history of data.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

AWS Machine Learning Blog

MARCH 10, 2023

This blog post will go through how data professionals may use SageMaker Data Wrangler’s visual interface to locate and connect to existing Amazon EMR clusters with Hive endpoints. To get ready for modeling or reporting, they can visually analyze the database, tables, schema, and author Hive queries to create the ML dataset.

Clustering 102

Clustering AWS ML ML 102

ODSC - Open Data Science

FEBRUARY 5, 2025

Common Pitfalls in LLM Development Neglecting Data Preparation: Poorly prepared data leads to subpar evaluation and iterations, reducing generalizability and stakeholder confidence. Real-world applications often expose gaps that proper data preparation could have preempted. Evaluation: Tools likeNotion.

Data Preparation 52

Data Preparation AI AI Data Scientist 52

AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. Data professionals such as data scientists want to use the power of Apache Spark , Hive , and Presto running on Amazon EMR for fast data preparation; however, the learning curve is steep.

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AWS Data Lakes Clustering Data Preparation 98

AWS Machine Learning Blog

JUNE 17, 2024

With SageMaker Processing jobs, you can use a simplified, managed experience to run data preprocessing or postprocessing and model evaluation workloads on the SageMaker platform. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB. All pipeline parameters used in this solution exist in a single config.yml file.

ML 125

ML ML AWS Machine Learning 125

Dataconomy

JULY 28, 2023

These tools offer a wide range of functionalities to handle complex data preparation tasks efficiently. The tool also employs AI capabilities for automatically providing attribute names and short descriptions for reports, making it easy to use and efficient for data preparation.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

FEBRUARY 4, 2024

The platform employs an intuitive visual language, Alteryx Designer, streamlining data preparation and analysis. With Alteryx Designer, users can effortlessly input, manipulate, and output data without delving into intricate coding, or with minimal code at most. Alteryx’s core features 1.

Data Preparation 52

Data Preparation Tableau Data Visualization Analytics 52

phData

AUGUST 2, 2024

Snowflake stored procedures are programmable routines that allow users to encapsulate and execute complex logic directly in a Snowflake database. Snowflake stored procedures support multiple programming languages (JavaScript, Python, and SQL) to meet different development needs and preferences.

Data Pipeline 52

Data Pipeline Python Database SQL 52

phData

DECEMBER 4, 2023

More on this topic later; but for now, keep in mind that the simplest method is to create a naming convention for database objects that allows you to identify the owner and associated budget. The extended period will allow you to perform Time Travel activities, such as undropping tables or comparing new data against historical values.

Clustering 98

Clustering Database SQL Data Pipeline 98

Flipboard

MAY 21, 2025

It offers cost-effective vector storage in SQL, combining fast searches with relational database features. Serverless architectures and tools like AWS CDK have helped teams to minimize infrastructure management, focusing instead on data preparation and prompt engineering. Security remains a cornerstone of this workflow.

AWS 78

AWS AI AI Database 78

DataRobot

JUNE 9, 2021

This often unduly increases model pipeline complexity, with major nuances being the pulling and shifting of data from where your data lives in production databases and handing machine learning operations teams a series of model artifacts with dependencies that need to be reassembled to put that model into production.

Machine Learning 52

Machine Learning Machine Learning Database Data Preparation 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

AWS 125

AWS Machine Learning Machine Learning ML 125

phData

SEPTEMBER 28, 2023

Dataflows represent a cloud-based technology designed for data preparation and transformation purposes. Dataflows have different connectors to retrieve data, including databases, Excel files, APIs, and other similar sources, along with data manipulations that are performed using Online Power Query Editor.

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Power BI Data Preparation Machine Learning Machine Learning 78

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 a pandas DataFrame) but in the company’s data warehouse (e.g., documentation. Redshift).

SQL 52

SQL Database Data Scientist Python 52

AWS Machine Learning Blog

MAY 25, 2023

QuickSight connects to your data and combines data from many different sources, such as Amazon S3 and Athena. For our solution, we use Athena as the data source. Athena is an interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. Choose Create data source.

ML 101

ML ML AWS Database 101

phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

Analytics 52

Analytics Analytics Database Python 52

AWS Machine Learning Blog

APRIL 6, 2023

Import the data To import data from Athena, complete the following steps: On the Canvas console, choose Datasets in the navigation pane, then choose Import. Expand the Data Source menu and choose Athena. Choose the correct database and table that you want to import from. For this post, we use all the data in the table.

Machine Learning 105

Machine Learning Machine Learning ML ML 105

Pickl AI

APRIL 9, 2025

The importance of ETL tools is underscored by their ability to handle diverse data sources, from relational databases to cloud-based services. This capability allows organizations to consolidate disparate data into a unified repository for analytics and reporting, providing insights that can drive strategic decisions.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

AWS Machine Learning Blog

MAY 31, 2024

Challenges associated with these stages involve not knowing all touchpoints where data is persisted, maintaining a data pre-processing pipeline for document chunking, choosing a chunking strategy, vector database, and indexing strategy, generating embeddings, and any manual steps to purge data from vector stores and keep it in sync with source data.

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AWS Machine Learning Machine Learning Database 133

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 Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

JULY 12, 2023

Let’s explore some common examples to understand how it works in practice: Example 1: Filtering and Sorting One fundamental data manipulation task is filtering and sorting. This involves selecting specific rows or columns based on certain criteria and arranging the data in order.

Data Analysis 52

Data Analysis Data Analysis Database Clean Data 52

phData

JUNE 18, 2024

In this blog, we’ll explain why you should prepare your data before use in machine learning , how to clean and preprocess the data, and a few tips and tricks about data preparation. Why Prepare Data for Machine Learning Models? It may hurt it by adding in irrelevant, noisy data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52