AWS Big Data

FEBRUARY 29, 2024

First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses. For more details, refer to Monitoring in-production ML models at large scale using Amazon SageMaker Model Monitor.

Data Governance 91

Data Governance Unstructured Data Metadata Data Lake 91

AWS Big Data

APRIL 3, 2023

Tens of thousands of customers run business-critical workloads on Amazon Redshift , AWS’s fast, petabyte-scale cloud data warehouse delivering the best price-performance. With Amazon Redshift, you can query data across your data warehouse, operational data stores, and data lake using standard SQL.

Data Warehouse 95

Data Warehouse Testing Data Lake Data-driven 95

AWS Big Data

APRIL 25, 2024

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a managed orchestration service for Apache Airflow that you can use to set up and operate data pipelines in the cloud at scale. Apache Airflow is an open source tool used to programmatically author, schedule, and monitor sequences of processes and tasks, referred to as workflows.

Metadata 100

Metadata Data Processing Management Testing 100

Jen Stirrup

MAY 3, 2020

Azure Synapse is an Azure-based cloud analytics service that provides insights on all data across data warehouses and big data analytics systems. It combines familiar SQL technologies such as data warehousing with Spark technologies used in big data analytics.

Big Data 56

Big Data Data Warehouse Machine Learning Business Intelligence 56

AWS Big Data

MARCH 27, 2024

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. This also makes sure that end-users have the latest data available in Amazon Redshift shortly after the source files are available.

Testing 96

Testing Data Warehouse Metrics Cost-Benefit 96

Cloudera

DECEMBER 19, 2018

Data analytics priorities have shifted this year. Don’t blink or you might miss what leading organizations are doing to modernize their analytic and data warehousing environments. Natural language analytics and streaming data analytics are emerging technologies that will impact the market.

Data Warehouse 54

Data Warehouse Analytics Big Data Data Architecture 54

AWS Big Data

JUNE 6, 2023

These are six main steps in the data pipeline: Amazon EventBridge triggers an AWS Lambda function when the event pattern for AWS Glue Data Quality matches the defined rule. For more information, refer to Working with Query Results, Output Files, and Query History. For S3 path , enter the S3 path to your data source. (

Data Quality 68

Data Quality Metrics Visualization Dashboards 68

AWS Big Data

APRIL 4, 2024

Griffin is an open source data quality solution for big data, which supports both batch and streaming mode. In today’s data-driven landscape, where organizations deal with petabytes of data, the need for automated data validation frameworks has become increasingly critical.

Data Quality 60

Data Quality Data Lake Data Warehouse Data-driven 60

AWS Big Data

JANUARY 24, 2024

These formats enable ACID (atomicity, consistency, isolation, durability) transactions, upserts, and deletes, and advanced features such as time travel and snapshots that were previously only available in data warehouses. For more information, refer to Amazon S3: Allows read and write access to objects in an S3 Bucket.

Snapshot 96

Snapshot Data Lake Metadata Optimization 96

AWS Big Data

JANUARY 8, 2024

This is the first post to a blog series that offers common architectural patterns in building real-time data streaming infrastructures using Kinesis Data Streams for a wide range of use cases. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

Analytics 108

Analytics IoT Data-driven Snapshot 108

AWS Big Data

MAY 2, 2023

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to analyze all your data efficiently and securely. Users such as data analysts, database developers, and data scientists use SQL to analyze their data in Amazon Redshift data warehouses.

Data Warehouse 60

Data Warehouse Software Visualization IoT 60

AWS Big Data

DECEMBER 13, 2023

A CDC-based approach captures the data changes and makes them available in data warehouses for further analytics in real-time. usually a data warehouse) needs to reflect those changes in near real-time. This post showcases how to use streaming ingestion to bring data to Amazon Redshift.

Data Warehouse 94

Data Warehouse Snapshot Data Processing Management 94

AWS Big Data

MAY 18, 2023

You can discover and connect to over 70 diverse data sources, manage your data in a centralized data catalog, and create, run, and monitor data integration pipelines to load data into your data lakes and your data warehouses. For more details, refer to Spark Release 3.3.0 runtime ( 3.5

Testing 73

Testing Data Lake Cost-Benefit Data Integration 73

AWS Big Data

MARCH 25, 2024

To learn more about RAG, refer to Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart. A RAG-based generative AI application can only produce generic responses based on its training data and the relevant documents in the knowledge base.

Data Lake 86

Data Lake Unstructured Data Management Modeling 86

Ontotext

AUGUST 22, 2023

The age of Big Data inevitably brought computationally intensive problems to the enterprise. Central to today’s efficient business operations are the activities of data capturing and storage, search, sharing, and data analytics. Get these wrong and chances are your enterprise processes and systems will suffer.

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

Smart Data Collective

APRIL 29, 2020

What is data management? Data management can be defined in many ways. Usually the term refers to the practices, techniques and tools that allow access and delivery through different fields and data structures in an organisation. Data transformation. Data analytics and visualisation.

Management 85

Management Data Warehouse Digital Transformation Dashboards 85

Jet Global

NOVEMBER 5, 2020

That stands for “bring your own database,” and it refers to a model in which core ERP data are replicated to a separate standalone database used exclusively for reporting. OLAP reporting has traditionally relied on a data warehouse. Data lakes move that step to the end of the process.

Data Lake 98

Data Lake OLAP Data Warehouse Unstructured Data 98

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? One challenge in applying data science is to identify pertinent business issues.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

AWS Big Data

APRIL 19, 2023

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. To configure AWS CLI interaction with AWS, refer to Quick setup. This refers to the step_id in the metadata entered in DynamoDB. X Python 3.8 Amazon EMR 6.1 etl_driver.py

Metadata 76

Metadata Testing Data Lake Consulting 76

Sisense

APRIL 26, 2020

Companies and businesses focus a lot on data collection in order to make sure they can get valuable insights out of it. Understanding data structure is a key to unlocking its value. A data’s “structure” refers to a particular way of organizing and storing it in a database or warehouse so that it can be accessed and analyzed.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

AWS Big Data

DECEMBER 21, 2023

As the volume and complexity of analytics workloads continue to grow, customers are looking for more efficient and cost-effective ways to ingest and analyse data. OpenSearch Service is used for multiple purposes, such as observability, search analytics, consolidation, cost savings, compliance, and integration.

Analytics 92

Analytics IT Data Lake Visualization 92

AWS Big Data

JULY 31, 2023

This solution decouples the ETL and analytics workloads from our transactional data source Amazon Aurora, and uses Amazon Redshift as the data warehouse solution to build a data mart. Please refer CDC support in DMS to extend the solutions for ongoing CDC. The components of TPCH consist of eight tables.

Sales 52

Sales Data Warehouse Visualization Testing 52

AWS Big Data

MARCH 27, 2024

These services enable you to collect and analyze data in near real time and put a comprehensive data governance framework in place that uses granular access control to secure sensitive data from unauthorized users. This will be your online transaction processing (OLTP) data store for transactional data.

Data Analytics 87

Data Analytics Analytics Data Warehouse Data Lake 87

Cloudera

AUGUST 2, 2018

True – millions of credit card transactions are processed within minutes for consistency, fraud and compliance, using petabytes of historical transactions as reference data. In each of the cases outlined above, the technology enabler is a new generation of data warehouses. We call it ‘Modern Data Warehousing’.

Data Warehouse 53

Data Warehouse Insurance Experimentation Software 53

datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. Today, big data is about business disruption.

IT 317

IT Statistics KPI Data-driven 317

Laminar Security

DECEMBER 13, 2023

They offer a comprehensive solution to enhance your cloud security posture and effectively manage your data. The primary focus of discovery is to find all the places where data exists and identify the assets it resides in. It helps in determining what data you have and its sensitivity.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

AWS Big Data

JUNE 29, 2023

Amazon Kinesis Data Analytics makes it easy to transform and analyze streaming data in real time. In this post, we discuss why AWS recommends moving from Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics for Apache Flink to take advantage of Apache Flink’s advanced streaming capabilities.

Data Analytics 71

Data Analytics Analytics IoT Data Lake 71

BizAcuity

APRIL 14, 2022

Big Data technology in today’s world. Did you know that the big data and business analytics market is valued at $198.08 Or that the US economy loses up to $3 trillion per year due to poor data quality? quintillion bytes of data which means an average person generates over 1.5 Big Data Ecosystem.

Big Data 52

Big Data Data Analytics Management Unstructured Data 52

datapine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. To help you improve your business intelligence engineer resume, or as it’s sometimes referred to, ‘resume BI engineer’, you should explore this BI resume example for guidance that will help your application get noticed by potential employers.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

AWS Big Data

NOVEMBER 29, 2023

As part of the Talent Intelligence Platform Eightfold also exposes a data hub where each customer can access their Amazon Redshift-based data warehouse and perform ad hoc queries as well as schedule queries for reporting and data export. Many customers have implemented Amazon Redshift to support multi-tenant applications.

Metadata 100

Metadata Data Warehouse Analytics Data Analytics 100

AWS Big Data

JANUARY 24, 2023

In this post, you will learn how to build a serverless analytics application using Amazon Redshift Data API and Amazon API Gateway WebSocket and REST APIs. The Data API simplifies access to Amazon Redshift because you don’t need to configure drivers and manage database connections.

Analytics 57

Analytics Data-driven Management Reporting 57

AWS Big Data

MAY 30, 2023

Data lakes are not transactional by default; however, there are multiple open-source frameworks that enhance data lakes with ACID properties, providing a best of both worlds solution between transactional and non-transactional storage mechanisms. The reference data is continuously replicated from MySQL to DynamoDB through AWS DMS.

Data Lake 76

Data Lake Data Analytics Analytics Data Processing 76

AWS Big Data

NOVEMBER 1, 2023

Amazon Redshift is a fully managed, petabyte scale cloud data warehouse that enables you to analyze large datasets using standard SQL. Data warehouse workloads are increasingly being used with mission-critical analytics applications that require the highest levels of resilience and availability.

Data Warehouse 91

Data Warehouse Snapshot Testing Management 91

Jet Global

OCTOBER 30, 2020

That means many of the reporting tools that customers previously used to access Microsoft Dynamics AX data will no longer work with D365 F&SCM. We refer to the first as “data entities.” You can think of data entities as a kind of translation layer or gatekeeper. What are unstructured data?

Data Lake 52

Data Lake Unstructured Data Reporting Finance 52

AWS Big Data

JUNE 28, 2023

For more details, refer to the What’s New Post. In this post, we provide step-by-step guidance on how to get started with near-real time operational analytics using this feature. There are two broad approaches to analyzing operational data for these use cases: Analyze the data in-place in the operational database (e.g.

Data Warehouse 81

Data Warehouse Analytics Metrics Dashboards 81

AWS Big Data

OCTOBER 18, 2023

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that provides the flexibility to use provisioned or serverless compute for your analytical workloads. Modern analytics is much wider than SQL-based data warehousing. Modern analytics is much wider than SQL-based data warehousing.

Analytics 92

Analytics Data Warehouse Testing Dashboards 92

AWS Big Data

JULY 25, 2023

Today, in order to accelerate and scale data analytics, companies are looking for an approach to minimize infrastructure management and predict computing needs for different types of workloads, including spikes and ad hoc analytics. Prerequisites To complete the integration, you need a Redshift Serverless data warehouse.

Data Analytics 52

Data Analytics Data Warehouse Analytics Data Processing 52