Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. It will enable you to quickly transform and load the data results into Amazon S3 data lakes or JDBC data stores.

Apache Kafka 130

Apache Kafka ETL Data Lakes AWS 130

Data Science 101

NOVEMBER 11, 2019

Azure Synapse Analytics This is the future of data warehousing. It combines data warehousing and data lakes into a simple query interface for a simple and fast analytics service. AWS Parallel Cluster for Machine Learning AWS Parallel Cluster is an open-source cluster management tool. Google Cloud.

Cloud Data 95

Cloud Data Data Science Azure Clustering 95

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 102

Tableau Data Lakes Data Warehouse SQL 102

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). Airflow An open-source platform for building and scheduling data pipelines.

Data Engineering 195

Data Engineering Data Engineer Data Engineering Data Engineering 195

Flipboard

NOVEMBER 17, 2023

Set up a MongoDB cluster To create a free tier MongoDB Atlas cluster, follow the instructions in Create a Cluster. Delete the MongoDB Atlas cluster. Prior joining AWS, as a Data/Solution Architect he implemented many projects in Big Data domain, including several data lakes in Hadoop ecosystem.

K-nearest Neighbors 170

K-nearest Neighbors AWS Clustering Database 170

Smart Data Collective

FEBRUARY 23, 2022

Traditional relational databases provide certain benefits, but they are not suitable to handle big and various data. That is when data lake products started gaining popularity, and since then, more companies introduced lake solutions as part of their data infrastructure. How to improve indexing.

Data Lakes 124

Data Lakes AWS SQL Big Data 124

phData

NOVEMBER 9, 2023

A data warehouse is a centralized and structured storage system that enables organizations to efficiently store, manage, and analyze large volumes of data for business intelligence and reporting purposes. What is a Data Lake? What is the Difference Between a Data Lake and a Data Warehouse?

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

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.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

phData

NOVEMBER 8, 2024

Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. However, this feature becomes an absolute must-have if you are operating your analytics on top of your data lake or lakehouse. It can also be integrated into major data platforms like Snowflake. Contact phData Today!

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

IBM Journey to AI blog

SEPTEMBER 25, 2023

When a query is constructed, it passes through a cost-based optimizer, then data is accessed through connectors, cached for performance and analyzed across a series of servers in a cluster. Because of its distributed nature, Presto scales for petabytes and exabytes of data.

Data Lakes 102

Data Lakes Analytics Analytics Clustering 102

ODSC - Open Data Science

APRIL 24, 2023

Botnet Detection at Scale — Lessons Learned From Clustering Billions of Web Attacks Into Botnets Editor’s note: Ori Nakar is a speaker for ODSC Europe this June. Be sure to check out his talk, “ Botnet detection at scale — Lesson learned from clustering billions of web attacks into botnets ,” there!

Clustering 52

Clustering SQL Algorithm Data Science 52

AWS Machine Learning Blog

DECEMBER 18, 2023

Prerequisites For this solution we use MongoDB Atlas to store time series data, Amazon SageMaker Canvas to train a model and produce forecasts, and Amazon S3 to store data extracted from MongoDB Atlas. The following screenshots shows the setup of the data federation. Setup the Database access and Network access.

Clustering 122

Clustering AWS Database ML 122

Smart Data Collective

FEBRUARY 20, 2022

Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps. Well, let’s find out. Artificial intelligence (AI). Messages and notification.

AWS 138

AWS Cloud Computing Data Lakes Database 138

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. Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster.

ML 123

ML ML AWS Data Warehouse 123

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

AWS Machine Learning Blog

FEBRUARY 21, 2025

Despite the benefits of this architecture, Rocket faced challenges that limited its effectiveness: Accessibility limitations: The data lake was stored in HDFS and only accessible from the Hadoop environment, hindering integration with other data sources. This also led to a backlog of data that needed to be ingested.

Data Science 77

Data Science AWS Hadoop Data Scientist 77

AWS Machine Learning Blog

SEPTEMBER 12, 2024

Organizations that want to build their own models or want granular control are choosing Amazon Web Services (AWS) because we are helping customers use the cloud more efficiently and leverage more powerful, price-performant AWS capabilities such as petabyte-scale networking capability, hyperscale clustering, and the right tools to help you build.

AWS 97

AWS AI AI Clustering 97

Pickl AI

DECEMBER 2, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Women in Big Data

NOVEMBER 27, 2024

Architecture At its core, Redshift consists of clusters made up of compute nodes, coordinated by a leader node that manages communications, parses queries, and executes plans by distributing tasks to the compute nodes. Security features include data encryption and access control.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

AWS Machine Learning Blog

JUNE 21, 2024

eSentire has over 2 TB of signal data stored in their Amazon Simple Storage Service (Amazon S3) data lake. This further step updates the FM by training with data labeled by security experts (such as Q&A pairs and investigation conclusions). They needed no additional infrastructure for data integration.

AWS 127

AWS AI AI Natural Language Processing 127

phData

SEPTEMBER 20, 2023

By offering native data engineering functionality inside of Snowflake virtual warehouses, we see a thriving data ecosystem being built around Snowflake. The release of Snowpark makes our customers’ lives simpler by unifying their data lake into a complete data platform.

SQL 98

SQL Python Data Lakes Machine Learning 98

ODSC - Open Data Science

JUNE 7, 2023

Wednesday, June 14th Me, my health, and AI: applications in medical diagnostics and prognostics: Sara Khalid | Associate Professor, Senior Research Fellow, Biomedical Data Science and Health Informatics | University of Oxford Iterated and Exponentially Weighted Moving Principal Component Analysis : Dr. Paul A.

Apache Kafka 52

Apache Kafka Machine Learning Machine Learning Data Science 52

Iguazio

JUNE 25, 2023

Clustering Metrics Clustering is an unsupervised learning technique where data points are grouped into clusters based on their similarities or proximity. Evaluation metrics include: Silhouette Coefficient - Measures the compactness and separation of clusters.

ML 52

ML ML Clustering Cross Validation 52

AWS Machine Learning Blog

AUGUST 15, 2024

You need data engineering expertise and time to develop the proper scripts and pipelines to wrangle, clean, and transform data. Afterward, you need to manage complex clusters to process and train your ML models over these large-scale datasets. Explore the future of no-code ML with SageMaker Canvas today.

ML 126

ML ML Data Preparation AWS 126

Pickl AI

FEBRUARY 21, 2023

The gathering of data requires assessment and research from various sources. The data locations may come from the data warehouse or data lake with structured and unstructured data. Data Preparation: the stage prepares the data collected and gathered for preparation for data mining.

Data Mining 52

Data Mining Data Mining Data Mining Data Scientist 52

DagsHub

NOVEMBER 11, 2024

Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data. These capture the semantic relationships between words, facilitating tasks like classification and clustering within ETL pipelines. This will ensure the data is in an ideal structure for further analysis.

AI 52

AI AI Data Lakes Database 52

ODSC - Open Data Science

JULY 22, 2023

Andre Franca | VP of Research and Development | causaLens Popular virtual sessions: AI and Bias: How to Detect It and How to Prevent It: Sandra Wachter, PhD | Professor, Technology and Regulation | Oxford Internet Institute, University of Oxford Probabilistic Machine Learning for Finance and Investing: Deepak Kanungo | Founder and CEO, Advisory Board (..)

Apache Kafka 52

Apache Kafka Machine Learning Machine Learning Data Science 52

Pickl AI

JULY 30, 2024

Flexibility : NiFi supports a wide range of data sources and formats, allowing organizations to integrate diverse systems and applications seamlessly. Scalability : NiFi can be deployed in a clustered environment, enabling organizations to scale their data processing capabilities as their data needs grow.

ETL 52

ETL Data Lakes Big Data Big Data 52

phData

DECEMBER 4, 2023

Thirty seconds is a good default for human users; if you find that queries are regularly queueing, consider making your warehouse a multi-cluster that scales on-demand. Cluster Count If your warehouse has to serve many concurrent requests, you may need to increase the cluster count to meet demand.

Clustering 98

Clustering Database SQL Data Pipeline 98

AWS Machine Learning Blog

MAY 15, 2023

Algorithm Selection Amazon Forecast has six built-in algorithms ( ARIMA , ETS , NPTS , Prophet , DeepAR+ , CNN-QR ), which are clustered into two groups: statististical and deep/neural network. Then the Step Functions “WaitInProgress” pipeline is triggered for each country, which enables parallel execution of a pipeline for each country.

Algorithm 96

Algorithm Data Scientist Machine Learning Machine Learning 96

Pickl AI

AUGUST 9, 2024

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. 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.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

ODSC - Open Data Science

JULY 14, 2023

The session participants will learn the theory behind compound sparsity, state-of-the-art techniques, and how to apply it in practice using the Neural Magic platform.

Machine Learning 52

Machine Learning Machine Learning Apache Kafka Data Science 52

Alation

JULY 18, 2022

Automatically tracking data lineage across queries executed in any language. To ensure you can deliver on this world-changing vision of data, Alation helps you maximize the value of your data lake with integrations to the Unity catalog. An information scheme in the Lakehouse. … and much more!

AI 52

AI AI Data Lakes Azure 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. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

ODSC - Open Data Science

JULY 11, 2023

What are the similarities and differences between data centers, data lake houses, and data lakes? Data centers, data lake houses, and data lakes are all related to data storage and management, but they have some key differences. Not a cloud computer?

Data Lakes 40

Data Lakes AI AI Cloud Computing 40

phData

APRIL 2, 2024

By leveraging cloud-based data platforms such as Snowflake Data Cloud , these commercial banks can aggregate and curate their data to understand individual customer preferences and offer relevant and personalized products. so that organizations can focus on delivering value rather than be burdened by operational complexities.

ML 52

ML ML Data Silos Data Lakes 52