Flipboard

DECEMBER 11, 2024

Second, because data, code, and other development artifacts like machine learning (ML) models are stored within different services, it can be cumbersome for users to understand how they interact with each other and make changes. With the SQL editor, you can query data lakes, databases, data warehouses, and federated data sources.

SQL 160

SQL AWS Data Lakes AI 160

ODSC - Open Data Science

APRIL 6, 2023

Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL. But why is SQL, or Structured Query Language , so important to learn? Let’s start with the first clause often learned by new SQL users, the WHERE clause.

SQL 98

SQL Data Scientist Database Data Science 98

AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. Apache HBase was employed to offer real-time key-based access to data.

Data Science 80

Data Science AWS Hadoop Data Scientist 80

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

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!

Database 158

Database AWS ETL SQL 158

How to Learn Machine Learning

APRIL 26, 2025

The processes of SQL, Python scripts, and web scraping libraries such as BeautifulSoup or Scrapy are used for carrying out the data collection. Tools like Python (with pandas and NumPy), R, and ETL platforms like Apache NiFi or Talend are used for data preparation before analysis. How to Choose the Right Data Science Career Path?

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

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. The following reference architecture depicts a workflow using ML with geospatial data.

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Clustering AWS ML ML 132

Applied Data Science

AUGUST 2, 2021

The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data. They build production-ready systems using best-practice containerisation technologies, ETL tools and APIs. Give this technique a try to take your team’s ML modelling to the next level.

Data Science 130

Data Science Data Scientist ML ML 130

AWS Machine Learning Blog

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. To learn more, see the documentation.

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AWS ML ML Data Quality 93

AWS Machine Learning Blog

SEPTEMBER 18, 2024

What Zeta has accomplished in AI/ML In the fast-evolving landscape of digital marketing, Zeta Global stands out with its groundbreaking advancements in artificial intelligence. Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment.

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AWS Machine Learning Machine Learning ML 120

The MLOps Blog

SEPTEMBER 7, 2023

This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc.

Machine Learning 52

Machine Learning Machine Learning ETL ML 52

IBM Journey to AI blog

MARCH 14, 2024

Db2 Warehouse fully supports open formats such as Parquet, Avro, ORC and Iceberg table format to share data and extract new insights across teams without duplication or additional extract, transform, load (ETL). This allows you to scale all analytics and AI workloads across the enterprise with trusted data. 

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AWS Database ETL AI 93

The MLOps Blog

JANUARY 26, 2024

Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?

Machine Learning 72

Machine Learning Machine Learning ML ML 72

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

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. ML training pipeline. The following sections explain the components involved in the solution.

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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. You can further fine-tune a generative AI model to tailor the model’s performance to your specific domain or task.

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The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams. DVC Git LFS neptune.ai

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ML ML Data Lakes Machine Learning 52

The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Stefan: Yeah.

ML 52

ML ML Data Scientist 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. On the client side, Snowpark consists of libraries, including the DataFrame API and native Snowpark machine learning (ML) APIs for model development (public preview) and deployment (private preview).

Python 52

Python ML ML SQL 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. SQL excels with big data and statistics, making it important in order to query databases.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Alation

MAY 24, 2022

Higher data intelligence drives higher confidence in everything related to analytics and AI/ML. SmartSuggestions — In Compose, Alation’s SQL editor, AI-powered suggestions actively show query writers relevant data to use as they query. for the popular database SQL Server. are five digits to meet standards.

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Data Quality Data Governance ETL Data Observability 98

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. Account A is the data lake account that houses all the ML-ready data obtained through extract, transform, and load (ETL) processes. You can also query, explore, and visualize data from Amazon EMR.

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AWS Data Lakes Clustering Data Preparation 98

The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. For example, you typically have functions to read your data, run SQL queries, and preprocess, transform, or enrich your dataset. There are several ways to use SQl wit Jupyter notebooks.

SQL 52

SQL Database Data Scientist Python 52

Pickl AI

APRIL 6, 2023

Having a solid understanding of ML principles and practical knowledge of statistics, algorithms, and mathematics. 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 Engineering Data Engineer 52

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. If a typical ML project involves standard pre-processing steps – why not make it reusable? Uses secure protocols for data security.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

DagsHub

APRIL 7, 2024

These pipelines cover the entire lifecycle of an ML project, from data ingestion and preprocessing, to model training, evaluation, and deployment. Adopted from [link] In this article, we will first briefly explain what ML workflows and pipelines are. around the world to streamline their data and ML pipelines.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

phData

OCTOBER 17, 2023

There are a couple of major problems: The Complexity of Traditional/Custom Tooling and ETL The traditional method of building ETL pipelines involves building efficient, custom code using architectural patterns that fit the current use case. dbt allows you to write templated SQL using Jinja to create macros.

ETL 52

ETL Data Pipeline Data Engineering Data Engineer 52

IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. IBM watsonx.ai With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

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AI AI Machine Learning Machine Learning 74

IBM Journey to AI blog

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Lakes 73

Data Lakes AI AI Data Governance 73

phData

SEPTEMBER 4, 2024

ThoughtSpot is a cloud-based AI-powered analytics platform that uses natural language processing (NLP) or natural language query (NLQ) to quickly query results and generate visualizations without the user needing to know any SQL or table relations. Suppose your business requires more robust capabilities across your technology stack.

Analytics 52

Analytics Analytics SQL ETL 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. Views Views in GCP BigQuery are virtual tables defined by SQL query that can display the results of a query or be used as the base for other queries.

SQL 52

SQL Database Apache Hadoop Data Science 52

Alation

JANUARY 25, 2022

This “analysis” is made possible in large part through machine learning (ML); the patterns and connections ML detects are then served to the data catalog (and other tools), which these tools leverage to make people- and machine-facing recommendations about data management and data integrations.

DataOps 52

DataOps SQL ML ML 52

Alation

APRIL 4, 2023

Expanded Integration with Databricks Unity Catalog Unity Catalog is Databricks ’ governance and admin layer for all lakehouse data and AI assets, including files, tables, ML models, and dashboards. The people navigating these increasingly chaotic landscapes need a single place to find, understand, and use data with total confidence.

DataOps 52

DataOps Data Engineering Data Engineer Data Engineering 52

IBM Journey to AI blog

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Pickl AI

JULY 8, 2024

Database Extraction: Retrieval from structured databases using query languages like SQL. This step often involves: ETL Processes: Extracting, transforming, and loading data into a target system. Read More: Top ETL Tools: Unveiling the Best Solutions for Data Integration. All About Data Quality Framework & Its Implementation.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. This article will discuss managing unstructured data for AI and ML projects. You will learn the following: Why unstructured data management is necessary for AI and ML projects. How to properly manage unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

phData

SEPTEMBER 28, 2023

These Dataflows are crucial in fostering consistency and reducing the duplication of repetitive ETL (Extract, Transform, Load) steps, achieved by reusing transformations. We suggest establishing distinct Dataflows for various source types like on-premises, cloud, SQL Server, and Databricks.

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Power BI Data Preparation Machine Learning Machine Learning 78