Smart Data Collective

SEPTEMBER 8, 2021

The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements. ETL is one of the most integral processes required by Business Intelligence and Analytics use cases since it relies on the data stored in Data Warehouses to build reports and visualizations.

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ETL Hadoop Data Warehouse Data Pipeline 126

AWS Machine Learning Blog

FEBRUARY 5, 2025

The following diagram illustrates the data pipeline for indexing and query in the foundational search architecture. OpenSearch is a powerful, open-source suite that provides scalable and flexible tools for search, analytics, security monitoring, and observabilityall under the Apache 2.0 For data handling, 24 data nodes (r6gd.2xlarge.search

K-nearest Neighbors 84

K-nearest Neighbors Machine Learning Machine Learning Database 84

Data Science Dojo

AUGUST 11, 2023

The flexibility of Python extends to its ability to integrate with other technologies, enabling data scientists to create end-to-end data pipelines that encompass data ingestion, preprocessing, modeling, and deployment. There are many different types of models that can be used in data science.

Data Science 195

Data Science Python Data Scientist Decision Trees 195

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 is because you usually see Kafka producers publish data or push it towards a Kafka topic so that the application can consume the data.

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Apache Kafka ETL Data Lakes AWS 130

AWS Machine Learning Blog

APRIL 5, 2024

This analytical model provides accurate estimates of land surface temperature (LST) at a granular level, allowing Gramener to quantify changes in the UHI effect based on parameters (names of indexes and data used). It allocates cluster resources for the duration of the job and removes them upon job completion.

Clustering 133

Clustering ML ML AWS 133

Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. First, let’s explore the key attributes of each role: The Data Scientist Data scientists have a wealth of practical expertise building AI systems for a range of applications. The Data Engineer Not everyone working on a data science project is a data scientist.

Data Science 130

Data Science Data Scientist ML ML 130

phData

MAY 3, 2024

Leveraging real-time analytics to make informed decisions is the golden standard for virtually every business that collects data. If you have the Snowflake Data Cloud (or are considering migrating to Snowflake ), you’re a blog away from taking a step closer to real-time analytics.

Apache Kafka 52

Apache Kafka Analytics Analytics ETL 52

Flipboard

AUGUST 17, 2023

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster. We attached the IAM role to the Redshift cluster that we created earlier.

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ML ML AWS Data Warehouse 123

Dataconomy

MAY 16, 2023

Skills and qualifications required for the role To excel as a machine learning engineer, individuals need a combination of technical skills, analytical thinking, and problem-solving abilities. They work with raw data, transform it into a usable format, and apply various analytical techniques to extract actionable insights.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

phData

NOVEMBER 9, 2023

Data Warehousing: Snowflake is primarily built for data warehousing workloads, providing a centralized repository for storing and managing structured and semi-structured data from various sources. Real-time Data: Snowflake can ingest and process real-time data streams for applications requiring up-to-the-minute insights.

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

AWS Machine Learning Blog

FEBRUARY 24, 2023

Solution overview In brief, the solution involved building three pipelines: Data pipeline – Extracts the metadata of the images Machine learning pipeline – Classifies and labels images Human-in-the-loop review pipeline – Uses a human team to review results The following diagram illustrates the solution architecture.

ML 101

ML ML AWS Data Pipeline 101

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

The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. 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.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

AWS Machine Learning Blog

DECEMBER 15, 2023

Amazon SageMaker Canvas is a no-code ML workspace offering ready-to-use models, including foundation models, and the ability to prepare data and build and deploy custom models. In this post, we discuss how to bring data stored in Amazon DocumentDB into SageMaker Canvas and use that data to build ML models for predictive analytics.

Machine Learning 121

Machine Learning Machine Learning AWS ML 121

Women in Big Data

NOVEMBER 27, 2024

It consolidates data from various systems, such as transactional databases, CRM platforms, and external data sources, enabling organizations to perform complex queries and derive insights. By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

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.

Database 98

Database Clustering SQL Data Pipeline 98

AWS Machine Learning Blog

NOVEMBER 1, 2023

PII Detected tagged documents are fed into Logikcull’s search index cluster for their users to quickly identify documents that contain PII entities. The request is handled by Logikcull’s application servers hosted on Amazon EC2 and the servers communicates with the search index cluster to find the documents.

AWS 121

AWS Machine Learning Machine Learning ML 121

phData

FEBRUARY 21, 2025

A data warehouse acts as a single source of truth for an organization’s data, providing a unified view of its operations and enabling data-driven decision-making. A data warehouse enables advanced analytics, reporting, and business intelligence. Today, the cloud has revolutionized the potential for data.

Data Warehouse 52

Data Warehouse Cloud Data ETL Cloud Computing 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The financial services industry (FSI) is no exception to this, and is a well-established producer and consumer of data and analytics. These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). The union of advances in hardware and ML has led us to the current day.

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AWS ML ML Clustering 113

Pickl AI

NOVEMBER 4, 2024

Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. It involves developing data pipelines that efficiently transport data from various sources to storage solutions and analytical tools. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

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. Read more to know.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

ZOE is a multi-agent LLM application that integrates with multiple data sources to provide a unified view of the customer, simplify analytics queries, and facilitate marketing campaign creation. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

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AWS Machine Learning Machine Learning ML 121

AWS Machine Learning Blog

OCTOBER 23, 2024

Since joining SnapLogic in 2010, Greg has helped design and implement several key platform features including cluster processing, big data processing, the cloud architecture, and machine learning. He currently is working on Generative AI for data integration.

AI 89

AI AI Database AWS 89

phData

MARCH 26, 2024

With its columnar format and unique features, we know that the Snowflake Data Cloud is fantastic at analytical workloads. But what if Snowflake could handle transactional data as well? What insights could you derive from having your transactional and analytical data in one place? appeared first on phData.

Clustering 52

Clustering Internet of Things Analytics Analytics 52

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

MAY 15, 2024

As a Data Analyst, you’ve honed your skills in data wrangling, analysis, and communication. But the allure of tackling large-scale projects, building robust models for complex problems, and orchestrating data pipelines might be pushing you to transition into Data Science architecture.

Data Analyst 52

Data Analyst Data Scientist Data Science Machine Learning 52

The MLOps Blog

JUNE 27, 2023

Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. It provides tools and components to facilitate end-to-end ML workflows, including data preprocessing, training, serving, and monitoring. Check out the Kedro’s Docs.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

phData

APRIL 18, 2023

This is due to a fragmented ecosystem of data silos, a lack of real-time fraud detection capabilities, and manual or delayed customer analytics, which results in many false positives. Data movements lead to high costs of ETL and rising data management TCO.

Data Silos 52

Data Silos ETL Clustering Analytics 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

SoureForge recently connected with Arjuna Chala, associate vice president at HPCC Systems , where he is responsible for evangelizing the HPCC Systems data lake platform. HPCC Systems and Spark also differ in that they work with distinct parts of the big data pipeline. You describe HPCC Systems as a complete data lake platform.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

IBM Journey to AI blog

SEPTEMBER 4, 2024

Kafka helps simplify the communication between customers and businesses, using its data pipeline to accurately record events and keep records of orders and cancellations—alerting all relevant parties in real-time. Telecom Telecommunications companies use Apache for a variety of services.

Apache Kafka 58

Apache Kafka Internet of Things Data Pipeline Clustering 58

Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

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

Pickl AI

JUNE 7, 2024

Businesses might need to invest additional resources to fix data issues, integrate disparate systems, or replace the inadequate tool entirely. Long-Term Data Management Strategies Investing in the right ETL tool offers numerous long-term benefits. Read More: Advanced SQL Tips and Tricks for Data Analysts.

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ETL Data Quality Data Pipeline Data Warehouse 40

Pickl AI

JULY 30, 2024

Flow-Based Programming : NiFi employs a flow-based programming model, allowing users to create complex data flows using simple drag-and-drop operations. This visual representation simplifies the design and management of data pipelines.

ETL 52

ETL Data Lakes Big Data Big Data 52

phData

AUGUST 10, 2023

In data vault implementations, critical components encompass the storage layer, ELT technology, integration platforms, data observability tools, Business Intelligence and Analytics tools, Data Governance , and Metadata Management solutions. The most important reason for using DBT in Data Vault 2.0

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Pickl AI

DECEMBER 9, 2024

Introduction Big Data continues transforming industries, making it a vital asset in 2025. The global Big Data Analytics market, valued at $307.51 Turning raw data into meaningful insights helps businesses anticipate trends, understand consumer behaviour, and remain competitive in a rapidly changing world.

Big Data 52

Big Data Big Data Hadoop Apache Kafka 52

Iguazio

JANUARY 16, 2024

Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. First, the data lake is fed from a number of data sources.

ML 52

ML ML DataOps Data Scientist 52