AWS Big Data

FEBRUARY 16, 2024

Many customers are extending their data warehouse capabilities to their data lake with Amazon Redshift. They are looking to further enhance their security posture where they can enforce access policies on their data lakes based on Amazon Simple Storage Service (Amazon S3).

Data Lake 93

Data Lake Data Warehouse Testing Business Objectives 93

David Menninger's Analyst Perspectives

NOVEMBER 16, 2021

Talend data integration software offers an open and scalable architecture and can be integrated with multiple data warehouses, systems and applications to provide a unified view of all data. Its code generation architecture uses a visual interface to create Java or SQL code.

Management 311

Management Data Warehouse Data Quality Data Integration 311

AWS Big Data

FEBRUARY 16, 2023

Amazon Redshift is the most widely used data warehouse in the cloud, best suited for analyzing exabytes of data and running complex analytical queries. Amazon QuickSight is a fast business analytics service to build visualizations, perform ad hoc analysis, and quickly get business insights from your data.

Data Warehouse 76

Data Warehouse Sales Visualization Data Processing 76

AWS Big Data

MARCH 7, 2024

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 109

Data Lake Analytics Dashboards Metrics 109

CIO Business Intelligence

OCTOBER 13, 2023

Data architect role Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles, often in support of data or digital transformations. In some ways, the data architect is an advanced data engineer.

Data Architecture 131

Data Architecture Data Warehouse Statistics Visualization 131

AWS Big Data

JUNE 6, 2023

It provides insights and metrics related to the performance and effectiveness of data quality processes. In this post, we highlight the seamless integration of Amazon Athena and Amazon QuickSight , which enables the visualization of operational metrics for AWS Glue Data Quality rule evaluation in an efficient and effective manner.

Data Quality 68

Data Quality Metrics Visualization Dashboards 68

AWS Big Data

OCTOBER 19, 2023

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure.

Data Lake 109

Data Lake Data Warehouse Visualization Snapshot 109

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. AWS Glue provides both visual and code-based interfaces to make data integration effortless. Choose Create job and Visual ETL. Choose Create connection.

Analytics 93

Analytics IT Data Lake Visualization 93

AWS Big Data

FEBRUARY 29, 2024

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

Data Governance 91

Data Governance Unstructured Data Metadata Data Lake 91

AWS Big Data

DECEMBER 5, 2023

This dynamic tool, powered by AWS and CARTO, provided robust visualizations of which regions and populations were interacting with our survey, enabling us to zoom in quickly and address gaps in coverage. Figure 1: Workflow illustrating data ingesting, transformation, and visualization using Redshift and CARTO.

Measurement 79

Measurement Dashboards Data Warehouse Analytics 79

AWS Big Data

NOVEMBER 20, 2023

Use case A typical workload for AWS Glue for Apache Spark jobs is to load data from a relational database to a data lake with SQL-based transformations. The following is a visual representation of an example job where the number of workers is 10. workerUtilization showed 1.0 100%) based on the workload requirements.

Metrics 92

Metrics Data Lake Cost-Benefit Dashboards 92

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 80

Enterprise Data Warehouse Snapshot Cost-Benefit 80

Cloudera

OCTOBER 13, 2020

In the beginning, CDP ran only on AWS with a set of services that supported a handful of use cases and workload types: CDP Data Warehouse: a kubernetes-based service that allows business analysts to deploy data warehouses with secure, self-service access to enterprise data. That Was Then. This is Now.

Data Warehouse 90

Data Warehouse Machine Learning Visualization Data Lake 90

AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

AWS Big Data

JUNE 28, 2023

QuickSight makes it incredibly simple and intuitive to get to answers with Amazon QuickSight Q , which allows users to ask business questions about their data in natural language and receive answers quickly through data visualizations. Improving the zero-ETL performance is a continuous goal for AWS.

Analytics 66

Analytics Data Warehouse Data Lake Data-driven 66

AWS Big Data

JUNE 6, 2023

It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines. Data quality at rest focuses on validating the data stored in data lakes, databases, or data warehouses. The extracted data is stored in Amazon S3, which serves as the data lake.

Data Quality 66

Data Quality Data Lake Visualization Data-driven 66

Data Science 101

NOVEMBER 7, 2019

Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Synapse allows one to use SQL to query petabytes of data, both relational and non-relational, with amazing speed. Visual Studio Online. Here they are in my order of importance (based upon my opinion).

Data Science 106

Data Science Machine Learning Data Lake IoT 106

Ontotext

MAY 2, 2024

Storage-centric approach In the storage-centric approach, people try to address data silos by throwing everything in a data lake or a data warehouse. But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

BizAcuity

MAY 16, 2022

Whereas, integrating data sources can provide you with a picture of where your customer is coming from, how long they spend on your website, what can be improved in the entire buying process among others. Integrating data allows you to perform cross-database queries, which like portals provide you with endless possibilities.

Business Intelligence 52

Business Intelligence Data mining Visualization Data Lake 52

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 82

Data Science Data Analytics Prescriptive Analytics Analytics 82

Jen Stirrup

MAY 5, 2020

You can also use Azure Data Lake storage as well, which is optimized for high-performance analytics. It has native integration with other data sources, such as SQL Data Warehouse, Azure Cosmos, database storage, and even Azure Blob Storage as well. You can use Visual Studio, which is a home for many developers.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

AWS Big Data

MARCH 25, 2024

Both engines provide native ingestion support from Kinesis Data Streams and Amazon MSK via a separate streaming pipeline to a data lake or data warehouse for analysis. OpenSearch Service offers visualization capabilities powered by OpenSearch Dashboards and Kibana (1.5

Data Lake 86

Data Lake Unstructured Data Management Modeling 86

Cloudera

DECEMBER 21, 2020

Company data exists in the data lake. Data Catalog profilers have been run on existing databases in the Data Lake. A Cloudera Data Warehouse virtual warehouse with Cloudera Data Visualisation enabled exists. A Cloudera Data Engineering service exists. The Data Analyst.

Dashboards 97

Dashboards Visualization Data Warehouse Data Lake 97

BizAcuity

JULY 13, 2021

In many cases, source data is captured in various databases and the need for data consolidation arises and typically it takes around 6-9 months to complete, and with a high budget in terms of provisioning for servers, either in cloud or on-premise, licenses for data warehouse platform, reporting system, ETL tools, etc.

Optimization 52

Optimization Marketing Analytics Data Warehouse 52

AWS Big Data

JUNE 6, 2023

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning.

Data Quality 92

Data Quality Statistics Data Lake Visualization 92

DataKitchen

JANUARY 3, 2022

Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units. Business analysts sometimes perform data science, but usually, they integrate and visualize data and create reports and dashboards from data supplied by other groups.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

Sisense

APRIL 26, 2020

Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both. Structured vs unstructured data. However, both types of data play an important role in data analysis.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

Jet Global

JANUARY 15, 2021

As Microsoft focuses its reporting strategy around Power BI and Azure Data Lake services, Dynamics partners should carefully consider the implications of starting down the path that Microsoft is recommending. A non-developer can build a custom data warehouse with Jet Analytics in as little as 30 minutes.

Cost-Benefit 72

Cost-Benefit Data Lake Reporting OLAP 72

Jet Global

OCTOBER 7, 2020

It uses its own data mart, which cannot be customized in any way. Power BI is an analytical tool for data visualization and discovery. When working with D365 F&SCM data, it typically requires specialized programming skills to develop reports or to make changes to existing reports. Enterprise Business Intelligence.

Data Warehouse 90

Data Warehouse OLAP Reporting Finance 90

AWS Big Data

JANUARY 8, 2024

With Kinesis Data Streams, customers can continuously capture terabytes of time series data from thousands of sources for cleaning, enrichment, storage, analysis, and visualization. The raw data can be streamed to Amazon S3 for archiving. Grafana supports data sources that are storage backends for time series data.

Analytics 109

Analytics IoT Data-driven Snapshot 109

Cloudera

DECEMBER 19, 2018

The most common big data use case is data warehouse optimization. Big data architecture is used to augment different applications, operating alongside or in a discrete fashion with a data warehouse. A big data implementation may even replace a data warehouse entirely with a data lake.

Data Warehouse 52

Data Warehouse Analytics Big Data Data Architecture 52

Jedox

OCTOBER 22, 2020

In working with clients, these are some of the most common “pain points” I routinely address: Difficulty in extracting data out of legacy systems. Limited real-time analytics and visuals. Inability to get data quickly. Data accuracy concerns. More time spent accessing data vs. making data-driven decisions.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

AWS Big Data

APRIL 20, 2023

Apache Spark enables you to build applications in a variety of languages, such as Java, Scala, and Python, by accessing the data in your Amazon Redshift data warehouse. Amazon Redshift integration for Apache Spark helps developers seamlessly build and run Apache Spark applications on Amazon Redshift data.

Data Lake 74

Data Lake Data Warehouse Sales Data-driven 74

Sisense

OCTOBER 6, 2020

As quantitative data is always numeric, it’s relatively straightforward to put it in order, manage it, analyze it, visualize it, and do calculations with it. Spreadsheet software like Excel, Google Sheets, or traditional database management systems all mainly deal with quantitative data.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

Smart Data Collective

DECEMBER 13, 2022

However, computerization in the digital age creates massive volumes of data, which has resulted in the formation of several industries, all of which rely on data and its ever-increasing relevance. Data analytics and visualization help with many such use cases. It is the time of big data. Understand Your Audience.

Visualization 135

Visualization Key Performance Indicator Sales Data Lake 135

Alation

OCTOBER 21, 2021

Data Swamp vs Data Lake. When you imagine a lake, it’s likely an idyllic image of a tree-ringed body of reflective water amid singing birds and dabbling ducks. I’ll take the lake, thank you very much. Many organizations have built a data lake to solve their data storage, access, and utilization challenges.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Andrew White

OCTOBER 22, 2021

Analytics Tactics (known outcome/known data/BI/analytics v unknown outcome/unknown data/data science/ML) 11. Data Hub Strategy 10. Lakehouse (data warehouse and data lake working together) 8. Data Literacy, training, coordination, collaboration 8. Enterprise Data Manager 1.

IT 52

IT Data Lake Strategy Data Science 52