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 150

Computer Science Computer Science ML ML 150

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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AWS Data Lakes Clustering Data Preparation 98

Data Science Dojo

JULY 6, 2023

Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). Amazon Redshift allows data engineers to analyze large datasets quickly using massively parallel processing (MPP) architecture. It is known for its high performance and cost-effectiveness.

Data Engineering 195

Data Engineering Data Engineering Data Engineering Data Engineer 195

AWS Machine Learning Blog

MAY 31, 2024

With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using the Amazon Web Services (AWS) tools without having to manage infrastructure. The following screenshot shows what the upload looks like when it’s complete.

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AWS Machine Learning Machine Learning Database 133

Smart Data Collective

OCTOBER 19, 2022

You can only deploy DynamoDB on Amazon Web Services (AWS), and it does not support on-premise deployments. With DynamoDB, you are essentially locked into AWS as your cloud provider. MongoDB is deployable anywhere, and the MongoDB Atlas database-as-a-service can be deployed on AWS, Azure, and Google Cloud Platform (GCP).

Big Data 132

Big Data Big Data Database AWS 132

phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Models Data Modeling Data Warehouse 52

AWS Machine Learning Blog

MAY 4, 2023

Forecast uses ML to learn not only the best algorithm for each item, but also the best ensemble of algorithms for each item, automatically creating the best model for your data. The console and AWS CLI methods are best suited for quick experimentation to check the feasibility of time series forecasting using your data.

AWS 130

AWS ML ML Data Scientist 130

Women in Big Data

NOVEMBER 27, 2024

By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making. How to Choose a Data Warehouse for Your Big Data Choosing a data warehouse for big data storage necessitates a thorough assessment of your unique requirements.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

phData

MAY 25, 2023

The June 2021 release of Power BI Desktop introduced Custom SQL queries to Snowflake in DirectQuery mode. However, Snowflake runs better on Azure than it does on AWS – so even though it’s not the ideal situation, Microsoft still sees Azure consumption when organizations host Snowflake on Azure.

Power BI 84

Power BI Analytics Analytics Azure 84

Pickl AI

NOVEMBER 18, 2024

Advanced tools like AWS QuickSight support large datasets and growing businesses. Microsoft Power BI is a comprehensive business intelligence (BI) tool designed to help organisations turn raw data into meaningful insights. It supports various Visualisations and can connect to various SQL-based data sources.

Power BI 52

Power BI Tableau Data Analysis Data Analysis 52

Women in Big Data

DECEMBER 9, 2024

Lookers strength lies in its ability to connect to a wide variety of data sources. Examples include SQl, DWH, and Cloud based systems (Google Bigquery). With Looker, you can share dashboards and visualizations seamlessly across teams, providing stakeholders with access to real-time data.

Big Data 52

Big Data Big Data Power BI Tableau 52

Pickl AI

APRIL 6, 2023

Understand the fundamentals of data engineering: To become an Azure Data Engineer, you must first understand the concepts and principles of data engineering. Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required. Hands-on experience working with SQLDW and SQL-DB.

Azure 52

Azure Data Engineering Data Engineering Data Engineering 52

Cambridge Intelligence

OCTOBER 19, 2023

The answer probably depends more on the complexity of your queries than the connectedness of your data. Relational databases (with recursive SQL queries), document stores, key-value stores, etc., Multi-model databases combine graphs with two other NoSQL data models – document and key-value stores.

Database 59

Database Azure Analytics Analytics 59

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 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. Scalability: Designed to handle large volumes of data efficiently.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

AI 52

AI AI Data Analysis Data Analysis 52

Flipboard

MARCH 23, 2023

Estimates start at around $2 million, ranging up to $12 million or so for the newest (and largest) models. Facebook/Meta’s LLaMA, which is smaller than GPT-3 and GPT-4, is thought to have taken roughly one million GPU hours to train, which would cost roughly $2 million on AWS. However, very few companies need to build their own models.

AI 175

AI AI SQL Natural Language Processing 175

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. Uses secure protocols for data security. Cons Limited connectors.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

phData

JULY 18, 2023

Support for Numerous Data Sources: Fivetran supports over 200 data sources, including popular databases, applications, and cloud platforms like Salesforce, Google Analytics, SQL Server, Snowflake, and many more. Additionally, unsupported data sources can be integrated using Fivetran’s cloud function connectors.

Data Warehouse 52

Data Warehouse Analytics Analytics SQL 52

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. In contrast, such traditional query languages struggle to interpret unstructured data. This text has a lot of information, but it is not structured.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

phData

NOVEMBER 13, 2023

It’s about more than just looking at one project; dbt Explorer lets you see the lineage across different projects, ensuring you can track your data’s journey end-to-end without losing track of the details. Figure 3: Multi-project lineage graph with dbt explorer. Source: Dave Connor's Loom.

Database 52

Database Business Intelligence Business Intelligence Data Silos 52

DagsHub

APRIL 21, 2024

Model Deployment and Serving Platforms Some of the most popular tools for development, serving and scaling are as follows: Amazon SageMaker Developed by Amazon Web Services (AWS) , Amazon Sagemaker is a fully managed machine learning service that allows developers and data scientists to build, train, and deploy machine learning models at scale.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

The MLOps Blog

OCTOBER 3, 2023

You see them all the time with a headline like: “data science, machine learning, Java, Python, SQL, or blockchain, computer vision.” It’s almost like a specialized data processing and storage solution. For example, you can use BigQuery , AWS , or Azure. How awful are they?” It’s two things.

ML 52

ML ML Data Scientist Machine Learning 52

AWS Machine Learning Blog

APRIL 14, 2025

Text-to-SQL empowers people to explore data and draw insights using natural language, without requiring specialized database knowledge. Amazon Web Services (AWS) has helped many customers connect this text-to-SQL capability with their own data, which means more employees can generate insights.

SQL 83

SQL Database AWS Data Modeling 83

AWS Machine Learning Blog

MARCH 18, 2025

SQL is one of the key languages widely used across businesses, and it requires an understanding of databases and table metadata. This can be overwhelming for nontechnical users who lack proficiency in SQL. This application allows users to ask questions in natural language and then generates a SQL query for the users request.

SQL 107

SQL Database AI AI 107

Towards AI

JANUARY 22, 2025

Uncomfortable reality: In the era of large language models (LLMs) and AutoML, traditional skills like Python scripting, SQL, and building predictive models are no longer enough for data scientist to remain competitive in the market. Programming expertise: A medium/high proficiency in Python and SQL is enough.

Data Scientist 105

Data Scientist EDA Exploratory Data Analysis AI 105

AWS Machine Learning Blog

OCTOBER 11, 2024

With the Amazon Bedrock serverless experience, you can experiment with and evaluate top foundation models (FMs) for your use cases, privately customize them with your data using techniques such as fine-tuning and RAG, and build agents that run tasks using enterprise systems and data sources.

Database 106

Database AWS Clustering Data Lakes 106

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52