CIO Business Intelligence

JUNE 29, 2022

But the features in Power BI Premium are now more powerful than the functionality in Azure Analysis Services, so while the service isn’t going away, Microsoft will offer an automated migration tool in the second half of this year for customers who want to move their data models into Power BI instead. Azure Data Factory.

Data Lake 116

Data Lake Analytics Data Warehouse Machine Learning 116

AWS Big Data

APRIL 30, 2024

Amazon Q Developer can now generate complex data integration jobs with multiple sources, destinations, and data transformations. Generated jobs can use a variety of data transformations, including filter, project, union, join, and custom user-supplied SQL.

Data Integration 128

Data Integration Data Lake Data Warehouse Software 128

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 115

Data Lake Snapshot Big Data Data-driven 115

AWS Big Data

DECEMBER 16, 2024

With the ability to browse metadata, you can understand the structure and schema of the data source, identify relevant tables and fields, and discover useful data assets you may not be aware of. This new capability can simplify your data journey. To learn more, refer to Amazon SageMaker Unified Studio.

Visualization 89

Visualization Data Processing Testing Publishing 89

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 110

Enterprise Data Warehouse Data Lake Optimization 110

AWS Big Data

NOVEMBER 13, 2023

Amazon Redshift is a fully managed data warehousing service that offers both provisioned and serverless options, making it more efficient to run and scale analytics without having to manage your data warehouse. Additionally, data is extracted from vendor APIs that includes data related to product, marketing, and customer experience.

Data Warehouse 111

Data Warehouse Analytics Data Lake Data Science 111

AWS Big Data

AUGUST 19, 2024

Refer to Enabling AWS PrivateLink in the Snowflake documentation to verify the steps, required access level, and service level to set the configurations. For Data sources , search for and select Snowflake. To obtain the Snowflake PrivateLink account URL, refer to parameters obtained in the prerequisites. Choose Next.

Analytics 119

Analytics Data-driven Data Integration Data Lake 119

AWS Big Data

SEPTEMBER 5, 2024

Amazon Redshift , a warehousing service, offers a variety of options for ingesting data from diverse sources into its high-performance, scalable environment. If storing operational data in a data warehouse is a requirement, synchronization of tables between operational data stores and Amazon Redshift tables is supported.

IoT 111

IoT Data Warehouse Cost-Benefit Reporting 111

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. For setup instructions, refer to Getting started with Amazon OpenSearch Service. By the end of this post, your visual ETL job will resemble the following screenshot.

Analytics 111

Analytics IT Data Lake Visualization 111

AWS Big Data

MARCH 3, 2023

Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes.

Data Lake 98

Data Lake Dashboards Metrics Metadata 98

AWS Big Data

FEBRUARY 6, 2023

For more details on how to configure and schedule the log collector, refer to the yarn-log-collector GitHub repo. Transform the YARN job history logs from JSON to CSV After obtaining YARN logs, you run a YARN log organizer, yarn-log-organizer.py, which is a parser to transform JSON-based logs to CSV files.

Dashboards 97

Dashboards Optimization Data Lake Cost-Benefit 97

DataKitchen

DECEMBER 9, 2022

The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes. ChatGPT> DataOps is a term that refers to the set of practices and tools that organizations use to improve the quality and speed of data analytics and machine learning.

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. For an example, refer to How JPMorgan Chase built a data mesh architecture to drive significant value to enhance their enterprise data platform. Platform architects define a well-architected platform.

Data Warehouse 114

Data Warehouse KPI Optimization Cost-Benefit 114

AWS Big Data

JUNE 29, 2023

In our solution, we create a notebook to access automotive sensor data, enrich the data, and send the enriched output from the Kinesis Data Analytics Studio notebook to an Amazon Kinesis Data Firehose delivery stream for delivery to an Amazon Simple Storage Service (Amazon S3) data lake.

Data Analytics 101

Data Analytics Analytics IoT Data Lake 101

AWS Big Data

FEBRUARY 8, 2023

This approach doesn’t solve for data quality issues in source systems, and doesn’t remove the need to have a wholistic data quality strategy. For addressing data quality challenges in Amazon Simple Storage Service (Amazon S3) data lakes and data pipelines, AWS has announced AWS Glue Data Quality (preview).

Data Quality 97

Data Quality Testing Data Warehouse Unstructured Data 97

AWS Big Data

MAY 25, 2023

Data transformation plays a pivotal role in providing the necessary data insights for businesses in any organization, small and large. To gain these insights, customers often perform ETL (extract, transform, and load) jobs from their source systems and output an enriched dataset.

Reporting 93

Reporting Metrics Optimization Data Lake 93

AWS Big Data

APRIL 25, 2024

In the era of data, organizations are increasingly using data lakes to store and analyze vast amounts of structured and unstructured data. Data lakes provide a centralized repository for data from various sources, enabling organizations to unlock valuable insights and drive data-driven decision-making.

Optimization 72

Optimization Data Lake Cost-Benefit Reporting 72

IBM Big Data Hub

OCTOBER 16, 2023

AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It can be used with both on-premise and multi-cloud environments.

Risk 70

Risk Modeling Management Metadata 70

AWS Big Data

FEBRUARY 6, 2023

Refactoring coupled compute and storage to a decoupling architecture is a modern data solution. It enables compute such as EMR instances and storage such as Amazon Simple Storage Service (Amazon S3) data lakes to scale. Jiseong Kim is a Senior Data Architect at AWS ProServe.

Cost-Benefit 52

Cost-Benefit Data Lake Dashboards Big Data 52

Sisense

MAY 11, 2021

The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021!

Modeling 84

Modeling Big Data IoT Data Warehouse 84

AWS Big Data

JANUARY 27, 2023

For example, the Flink FileSystem connector has FileSystemTableFactory to read/write data in Hadoop Distributed File System (HDFS) or Amazon Simple Storage Service (Amazon S3), the Flink HBase connector has HBase2DynamicTableFactory to read/write data in HBase, and the Flink Kafka connector has KafkaDynamicTableFactory to read/write data in Kafka.

Data Lake 83

Data Lake Metadata Business Analysis Data-driven 83

AWS Big Data

AUGUST 1, 2023

Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other business data, as well as support the use of business intelligence (BI) tools and artificial intelligence (AI) and machine learning (ML) applications. Search for the Jira Cloud connector.

Data Lake 98

Data Lake Data Transformation Data-driven Cost-Benefit 98

AWS Big Data

OCTOBER 30, 2024

With this integration, you can now seamlessly query your governed data lake assets in Amazon DataZone using popular business intelligence (BI) and analytics tools, including partner solutions like Tableau. Refer to the detailed blog post on how you can use this to connect through various other tools.

Analytics 117

Analytics Visualization Data Governance Data-driven 117

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 122

Data Lake Management Metrics Data Warehouse 122

Cloudera

NOVEMBER 22, 2021

In addition, data pipelines include more and more stages, thus making it difficult for data engineers to compile, manage, and troubleshoot those analytical workloads. As a result, alternative data integration technologies (e.g., CRM platforms). benchmarking study conducted by independent 3rd party ).

Data Processing 94

Data Processing Data Warehouse Enterprise Visualization 94

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 103

Data Lake Testing Data Integration Metadata 103

AWS Big Data

NOVEMBER 14, 2024

We use the built-in features of Data Firehose, including AWS Lambda for necessary data transformation and Amazon Simple Notification Service (Amazon SNS) for near real-time alerts. APIs act as the entry point for applications to access data, business logic, or functionality from your backend services.

Data Lake 105

Data Lake Metadata Testing Data-driven 105

AWS Big Data

FEBRUARY 18, 2025

To optimize their security operations, organizations are adopting modern approaches that combine real-time monitoring with scalable data analytics. They are using data lake architectures and Apache Iceberg to efficiently process large volumes of security data while minimizing operational overhead.

Snapshot 78

Snapshot Optimization Data Lake Metadata 78

AWS Big Data

JANUARY 29, 2025

This configuration allows you to augment your sensitive on-premises data with cloud data while making sure all data processing and compute runs on-premises in AWS Outposts Racks. Additionally, we show you how to submit batch jobs to Amazon EMR using EMR steps for automated, scheduled data processing.

Big Data 86

Big Data Data Analytics Analytics Interactive 86

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