AWS Big Data

MAY 9, 2023

For workloads such as data transforms, joins, and queries, you can use G.1X With exponentially growing data sources and data lakes, customers want to run more data integration workloads, including their most demanding transforms, aggregations, joins, and queries. 1X (1 DPU) and G.2X You can enable G.4X

Data Lake 105

Data Lake Cost-Benefit Data Integration Data Transformation 105

AWS Big Data

JANUARY 17, 2024

With Amazon EMR 6.15, we launched AWS Lake Formation based fine-grained access controls (FGAC) on Open Table Formats (OTFs), including Apache Hudi, Apache Iceberg, and Delta lake. Many large enterprise companies seek to use their transactional data lake to gain insights and improve decision-making.

Data Lake 111

Data Lake Snapshot Big Data Data-driven 111

AWS Big Data

APRIL 26, 2023

To enable this use case, we used the BMW Group’s cloud-native data platform called the Cloud Data Hub. In 2019, the BMW Group decided to re-architect and move its on-premises data lake to the AWS Cloud to enable data-driven innovation while scaling with the dynamic needs of the organization.

Forecasting 135

Forecasting Manufacturing Data Lake Big Data 135

AWS Big Data

AUGUST 19, 2024

This report is essential for understanding revenue streams, identifying opportunities for optimization, and making data-driven decisions regarding pricing and promotions. Refer to Editing AWS Glue managed data transform nodes for more information. Stop any AWS Glue interactive sessions.

Analytics 112

Analytics Data-driven Data Integration Data Lake 112

AWS Big Data

DECEMBER 5, 2023

To fill in the gaps in existing data, HR&A creates digital equity surveys to build a more complete picture before developing digital equity plans. HR&A has used Amazon Redshift Serverless and CARTO to process survey findings more efficiently and create custom interactive dashboards to facilitate understanding of the results.

Measurement 98

Measurement Dashboards Data Warehouse Analytics 98

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 103

Enterprise Data Warehouse Data Lake Optimization 103

AWS Big Data

NOVEMBER 9, 2023

In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

Data Warehouse 122

Data Warehouse Testing Data Quality Reporting 122

IBM Big Data Hub

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Data lakehouse was created to solve these problems.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. Data engineers are crucial for schema conversion and data transformation, and DBAs can handle cluster configuration and workload monitoring. Platform architects define a well-architected platform.

Data Warehouse 105

Data Warehouse KPI Optimization Cost-Benefit 105

AWS Big Data

JUNE 29, 2023

Solution overview For our use case, we use several AWS services to stream, ingest, transform, and analyze sample automotive sensor data in real time using Kinesis Data Analytics Studio. Kinesis Data Analytics Studio allows us to create a notebook, which is a web-based development environment. View the stream data.

Data Analytics 90

Data Analytics Analytics IoT Data Lake 90

IBM Big Data Hub

OCTOBER 16, 2023

They are used in everything from robotics to tools that reason and interact with humans. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

Risk 70

Risk Modeling Management Metadata 70

IBM Big Data Hub

JULY 17, 2023

While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases. offers a Prompt Lab, where users can interact with different prompts using prompt engineering on generative AI models for both zero-shot prompting and few-shot prompting.

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

AWS Big Data

JANUARY 27, 2023

This post shows you how to integrate Apache Flink in Amazon EMR with the AWS Glue Data Catalog so that you can ingest streaming data in real time and access the data in near-real time for business analysis. For data read/write, Flink has the interface DynamicTableSourceFactory for read and DynamicTableSinkFactory for write.

Data Lake 71

Data Lake Metadata Business Analysis Data-driven 71

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 119

Data Lake Management Metrics Data Warehouse 119

IBM Big Data Hub

JULY 5, 2023

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale.

Data Lake 60

Data Lake Metadata Cost-Benefit Data Warehouse 60

CIO Business Intelligence

AUGUST 3, 2023

In the past, First Service Credit Union’s Chief data officer Ty Robbins struggled to integrate data from the legacy, non-relational, and often proprietary tabular databases on which many credit unions run. After moving its expensive, on-premise data lake to the cloud, Comcast created a three-tiered architecture.

Analytics 98

Analytics Data Lake Metadata Cost-Benefit 98

AWS Big Data

DECEMBER 21, 2023

To bring their customers the best deals and user experience, smava follows the modern data architecture principles with a data lake as a scalable, durable data store and purpose-built data stores for analytical processing and data consumption.

Data Lake 108

Data Lake Data Warehouse Data-driven B2B 108

Cloudera

OCTOBER 11, 2021

Modak Nabu reliably curates datasets for any line of business and personas, from business analysts to data scientists. Customers using Modak Nabu with CDP today have deployed Data Lakes and. This is the scale and speed that cloud-native solutions can provide — and Modak Nabu with CDP has been delivering the same.

Data Lake 95

Data Lake Cost-Benefit Data-driven Dashboards 95

CIO Business Intelligence

AUGUST 7, 2024

The initiative has enhanced coordination, as automation APIs facilitate interaction with security tools as well as streamline coordination and enhance mitigation responses. This is a new way to interact with the web and search. It then built a cutting-edge cloud-based analytics platform, designed with an innovative data architecture.

IT 119

IT Insurance Cost-Benefit Testing 119

IBM Big Data Hub

JUNE 15, 2023

It is comprised of commodity cloud object storage, open data and open table formats, and high-performance open-source query engines. To help organizations scale AI workloads, we recently announced IBM watsonx.data , a data store built on an open data lakehouse architecture and part of the watsonx AI and data platform.

Data Warehouse 86

Data Warehouse Data Lake Optimization Data-driven 86

AWS Big Data

NOVEMBER 7, 2023

Second, organizations still need transformations like cleansing, deduplication, and combining datasets for analysis and machine learning (ML). For these, AWS Glue provides fast, scalable data transformation. Business analysts use SageMaker Canvas to build ML models and generate predictions without needing to write code.

Dashboards 80

Dashboards Data-driven Data Integration Data Lake 80

AWS Big Data

APRIL 5, 2023

Showpad built new customer-facing embedded dashboards within Showpad eOSTM and migrated its legacy dashboards to Amazon QuickSight , a unified BI service providing modern interactive dashboards, natural language querying, paginated reports, machine learning (ML) insights, and embedded analytics at scale.

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52

AWS Big Data

DECEMBER 2, 2024

Using AWS Glue , a serverless data integration service, companies can streamline this process, integrating data from internal and external sources into a centralized AWS data lake. From there, they can perform meaningful analytics, gain valuable insights, and optionally push enriched data back to external SaaS platforms.

Data Lake 94

Data Lake Testing Data Integration Metadata 94

AWS Big Data

JANUARY 29, 2025

Amazon EMR has long been the leading solution for processing big data in the cloud. Amazon EMR is the industry-leading big data solution for petabyte-scale data processing, interactive analytics, and machine learning using over 20 open source frameworks such as Apache Hadoop , Hive, and Apache Spark.

Big Data 73

Big Data Data Analytics Analytics Interactive 73

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

FEBRUARY 23, 2024

This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making. Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Visualization 52