Dataconomy

APRIL 2, 2025

Clustering algorithms (K-Means) classify wallet activity to forecast shifts on a larger scale. These models usually combine on-chain data with social metrics and some macro variables to achieve a holistic view of market risk and momentum. Also, AI can analyze real-time data and provide risk assessments on the minute.

Data Science 103

Data Science Natural Language Processing Machine Learning Machine Learning 103

Smart Data Collective

JUNE 4, 2021

Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century.

Data Science 133

Data Science Exploratory Data Analysis Machine Learning Machine Learning 133

AWS Machine Learning Blog

SEPTEMBER 26, 2024

However, building large distributed training clusters is a complex and time-intensive process that requires in-depth expertise. It removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs).

Clustering 124

Clustering Algorithm ML ML 124

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. Apache HBase was employed to offer real-time key-based access to data. This created a challenge for data scientists to become productive. HBase is employed to offer real-time key-based access to data.

Data Science 84

Data Science AWS Hadoop Data Scientist 84

Data Science Dojo

JULY 6, 2023

It supports various data types and offers advanced features like data sharing and multi-cluster warehouses. Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). It allows data engineers to build, test, and maintain data pipelines in a version-controlled manner.

Data Engineering 195

Data Engineering Data Engineering Data Engineering Data Engineer 195

Hacker News

JUNE 6, 2025

This data model is well-suited for financial transactions, inventory, ticketing, or utility metering. 2 The Viewstamped Operation Replicator (VOPR) test simulates an entire TigerBeetle cluster, including clock, disk, and network interfaces. For example, the 0.16.21 binary can run 0.16.17, 0.16.18, and so on through 0.16.21.

Clustering 65

Clustering Database Data Models Data Modeling 65

Towards AI

APRIL 28, 2025

Tests setup We ran load tests on an Amazon EKS cluster using t2.medium medium instances (2 vCPUs, 4 GB RAM), hosting both the Locust deployment and the Ray cluster running Volga. Each Ray pod was mapped to a single EKS node to ensure resource isolation.

Clustering 59

Clustering AWS ML ML 59

Smart Data Collective

MAY 16, 2022

They are a part of the data management system. A database consists of data structures or data models which are used to store and organize information. Data models help in storing and retrieving the data efficiently.

Data Mining 136

Data Mining Data Mining Data Mining Data Warehouse 136

AWS Machine Learning Blog

SEPTEMBER 12, 2024

Thomson Reuters knew they would need to run a series of experiments—training LLMs from 7B to more than 30B parameters, starting with an FM and continuous pre-training (using various techniques) with a mix of Thomson Reuters and general data. Chinchilla point 52b 132b 260b 600b 1.3t So, for example, a 6.6B

Clustering 115

Clustering AWS ML ML 115

Smart Data Collective

JUNE 16, 2022

A data warehouse extracts data from a variety of sources and formats, including text files, excel sheets, multimedia files, and so on. The consolidated totals are saved in a data model in the HOLAP technique, while the particular data is maintained in a relational database.

Analytics 139

Analytics Analytics Data Scientist Data Warehouse 139

Dataversity

AUGUST 17, 2022

While a Cassandra table’s compaction strategy can be adjusted after its creation, doing so invites costly cluster performance penalties because Cassandra will need to rewrite all of that table’s data. Taking […]. The post A Primer to Optimizing Your Apache Cassandra Compaction Strategy appeared first on DATAVERSITY.

Clustering 98

Clustering Data Modeling Data Models Database 98

Hacker News

JULY 18, 2023

What if you could automatically shard your PostgreSQL database across any number of servers and get industry-leading performance at scale without any special data modelling steps? Schema-based sharding has almost no data modelling restrictions or special steps compared to unsharded PostgreSQL.

Database 123

Database SQL Data Modeling Data Models 123

DataRobot

DECEMBER 16, 2021

and train models with a single click of a button. Advanced users will appreciate tunable parameters and full access to configuring how DataRobot processes data and builds models with composable ML. Explanations around data, models , and blueprints are extensive throughout the platform so you’ll always understand your results.

AI 98

AI AI Exploratory Data Analysis Clustering 98

phData

OCTOBER 9, 2023

Flexibility and adaptability for evolving business requirements Simplified data integration and agility in data modeling Incremental loading and historical data tracking capabilities Enhanced scalability and performance through parallel processing To get more information on the benefits of Data Vault with Snowflake, check out our blog!

ETL 69

ETL Clustering Data Warehouse SQL 69

AWS Machine Learning Blog

AUGUST 21, 2023

The capabilities of Lake Formation simplify securing and managing distributed data lakes across multiple accounts through a centralized approach, providing fine-grained access control. Solution overview We demonstrate this solution with an end-to-end use case using a sample dataset, the TPC data model. compute.internal.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

Towards AI

APRIL 22, 2024

NoSQL databases — NoSQL is a vast category that includes all databases that do not use SQL as their primary data access language. These databases do not comply with ACID properties which poses a threat to the consistency of the data stored in the database.

Database 102

Database SQL Clustering Big Data 102

Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. regression, classification, clustering).

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines Decision Trees 52

AWS Machine Learning Blog

DECEMBER 12, 2023

Training steps To run the training, we use SLURM managed multi-node Amazon Elastic Compute Cloud ( Amazon EC2 ) Trn1 cluster, with each node containing a trn1.32xl instance. Next, we also evaluate the loss trajectory of the model training on AWS Trainium and compare it with the corresponding run on a P4d (Nvidia A100 GPU cores) cluster.

AWS 129

AWS Machine Learning Machine Learning Deep Learning 129

Pickl AI

SEPTEMBER 20, 2024

Both databases are designed to handle large volumes of data, but they cater to different use cases and exhibit distinct architectural designs. Cassandra’s architecture is based on a peer-to-peer model where all nodes in the cluster are equal. Partition Key: Determines how data is distributed across nodes in the cluster.

Database 52

Database Clustering Data Modeling Data Models 52

Cambridge Intelligence

OCTOBER 25, 2023

When you design your data model, you’ll probably begin by sketching out your data in a graph format – representing entities as nodes and relationships as links. Working in a graph database means you can take that whiteboard model and apply it directly to your schema with relatively few adaptations.

Database 64

Database Data Models Data Modeling Algorithm 64

Pickl AI

AUGUST 29, 2023

Model Selection: You need to choose an appropriate statistical model or technique that is based on the nature of the data and research question. This could be linear regression, logistic regression, clustering , time series analysis , etc. This may involve finding values that best represent to observed data.

Data Scientist 52

Data Scientist Clustering Data Analysis Data Analysis 52

phData

JULY 12, 2023

Businesses today are grappling with vast amounts of data coming from diverse sources. To effectively manage and harness this data, many organizations are turning to a data vault—a flexible and scalable data modeling approach that supports agile data integration and analytics.

Clustering 52

Clustering Data Warehouse Data Quality Data Modeling 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. It promotes a disciplined approach to data modeling, making it easier to ensure data quality and consistency across the ML pipelines.

AWS 126

AWS Machine Learning Machine Learning ML 126

Pickl AI

APRIL 3, 2023

Significantly, there are two types of Unsupervised Learning: Clustering: which involves grouping similar data points together. Effectively, some instances of unsupervised learning algorithms include k-means clustering, hierarchical clustering, principal component analysis (PCA), and association rule learning.

Supervised Learning 52

Supervised Learning Machine Learning Machine Learning Clustering 52

Tableau

DECEMBER 1, 2021

Clustered under visual encoding , we have topics of self-service analysis , authoring , and computer assistance. Connecting to data is fundamental to all data work, which is why “get data'' is at the start of the Cycle of Visual Analysis. Gestalt properties including clusters are salient on scatters. Connectivity.

Tableau 145

Tableau ML ML Database 145

AWS Machine Learning Blog

APRIL 25, 2024

We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud. This post delves into integrating Hugging Face’s PyAnnote for speaker diarization with Amazon SageMaker asynchronous endpoints. and requirements.txt files and save it as model.tar.gz : !

AWS 126

AWS ML ML Python 126

Mlearning.ai

MAY 16, 2023

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

FEBRUARY 13, 2024

By centralizing SAP ERP data in Snowflake, organizations can gain deeper insights into key business metrics, trends, and performance indicators, enabling more informed decision-making, strategic planning, and operational optimization. Violations of license restrictions can result in penalties, additional fees, or even legal consequences.

Analytics 59

Analytics Analytics Data Scientist Data Modeling 59

AWS Machine Learning Blog

APRIL 29, 2024

Besides easy access, using Trainium with Metaflow brings a few additional benefits: Infrastructure accessibility Metaflow is known for its developer-friendly APIs that allow ML/AI developers to focus on developing models and applications, and not worry about infrastructure.

AWS 129

AWS ML ML Python 129

AWS Machine Learning Blog

SEPTEMBER 19, 2024

Solution overview This post demonstrates the use of SageMaker Training for running torchtune recipes through task-specific training jobs on separate compute clusters. SageMaker Training is a comprehensive, fully managed ML service that enables scalable model training. and more. linear: layers.31.mlp.w1,

AWS 99

AWS ML ML Machine Learning 99

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. Variant columns can be used to store data that doesn’t fit neatly into traditional columns, such as nested data structures, arrays, or key-value pairs.

Data Warehouse 72

Data Warehouse Data Governance Clustering Database 72

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

SEPTEMBER 12, 2024

The server aggregates these updates to build a global model, which is then sent back to all clients for further refinement. How It Works Model Training : Each client trains a model locally on its private data. The cluster servers then communicate with a central server to form the final global model.

Machine Learning 52

Machine Learning Machine Learning Clustering Algorithm 52

Cambridge Intelligence

OCTOBER 19, 2023

Multi-model databases combine graphs with two other NoSQL data models – document and key-value stores. RDF vs property graphs Another way to categorize graph databases is by their data structure. RDF vs property graphs Another way to categorize graph databases is by their data structure.

Database 59

Database Azure Analytics Analytics 59

phData

AUGUST 10, 2023

Alternatively, you can create multiple streams and tasks from the same staging table to populate each data vault object using separate asynchronous flows. Data Vault Automation Working at scale can be challenging, especially when managing the data model. Implement Data Lineage and Traceability Path: Data Vault 2.0

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Mlearning.ai

APRIL 12, 2023

Using different machine learning algorithms for performance optimization: Several machine learning algorithms can be used for performance optimization, including regression, clustering, and decision trees. Clustering algorithms can be used to group users based on behavior patterns and optimize performance for each group.

Machine Learning 52

Machine Learning Machine Learning Decision Trees ML 52

Pickl AI

JANUARY 28, 2025

They are useful for big data analytics where flexibility is needed. Data Modeling Data modeling involves creating logical structures that define how data elements relate to each other. This includes: Dimensional Modeling : Organizes data into dimensions (e.g., time, product) and facts (e.g.,

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52