AWS Big Data

DECEMBER 13, 2023

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

Metadata 111

Metadata Data Lake Visualization Data Quality 111

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

AUGUST 9, 2024

With the ever-increasing volume of data available, Dafiti faces the challenge of effectively managing and extracting valuable insights from this vast pool of information to gain a competitive edge and make data-driven decisions that align with company business objectives. We started with 115 dc2.large

Data Lake 111

Data Lake Analytics Data Warehouse 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 145

Forecasting Manufacturing Data Lake Big Data 145

DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. You can use it for big data analytics and machine learning workloads.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

AWS Big Data

JANUARY 6, 2025

With Amazon AppFlow, you can run data flows at nearly any scale and at the frequency you chooseon a schedule, in response to a business event, or on demand. You can configure data transformation capabilities such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps.

Analytics 98

Analytics Data Warehouse Metrics Big Data 98

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. These upstream data sources constitute the data producer components.

Data Warehouse 111

Data Warehouse Analytics Data Lake Data Science 111

AWS Big Data

AUGUST 13, 2024

It enables you to visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes. We will create a glue studio job, add events and venue data from the SFTP server, carry out data transformations and load transformed data to s3.

Data Processing 115

Data Processing Visualization Data Lake Data Processing 115

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. This enables organizations to streamline data integration and analytics with OpenSearch Service. Reduce the waiting period to 7 days and schedule the deletion.

Analytics 111

Analytics IT Data Lake Visualization 111

AWS Big Data

APRIL 11, 2023

By collecting data from store sensors using AWS IoT Core , ingesting it using AWS Lambda to Amazon Aurora Serverless , and transforming it using AWS Glue from a database to an Amazon Simple Storage Service (Amazon S3) data lake, retailers can gain deep insights into their inventory and customer behavior.

Forecasting 98

Forecasting Management IoT Data-driven 98

AWS Big Data

NOVEMBER 9, 2023

It does this by helping teams handle the T in ETL (extract, transform, and load) processes. It allows users to write data transformation code, run it, and test the output, all within the framework it provides. This process has been scheduled to run daily, ensuring a consistent batch of fresh data for analysis.

Data Warehouse 126

Data Warehouse Testing Data Quality Reporting 126

AWS Big Data

AUGUST 19, 2024

Using AWS Glue transformations is crucial when creating an AWS Glue job because they enable efficient data cleansing, enrichment, and restructuring, making sure the data is in the desired format and quality for downstream processes. Refer to Editing AWS Glue managed data transform nodes for more information.

Analytics 119

Analytics Data-driven Data Integration Data Lake 119

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. Overall, DataOps is an essential component of modern data-driven organizations. Query> DataOps. Query> Write an essay on DataOps.

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

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

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 115

Data Warehouse KPI Optimization Cost-Benefit 115

AWS Big Data

DECEMBER 5, 2023

For files with known structures, a Redshift stored procedure is used, which takes the file location and table name as parameters and runs a COPY command to load the raw data into corresponding Redshift tables. We encourage you to explore Redshift Serverless with CARTO for analyzing spatial data and let us know your experience in the comments.

Measurement 105

Measurement Dashboards Data Warehouse Analytics 105

AWS Big Data

OCTOBER 27, 2023

Data integration is the foundation of robust data analytics. It encompasses the discovery, preparation, and composition of data from diverse sources. In the modern data landscape, accessing, integrating, and transforming data from diverse sources is a vital process for data-driven decision-making.

Analytics 124

Analytics Visualization Data Integration Cost-Benefit 124

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. These two functions are nearly inseparable as we move further into a world of analytics that blends sources of varying volume, variety, veracity, and velocity. Big data analytics case study: SkullCandy.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

AWS Big Data

FEBRUARY 6, 2023

Use case overview Migrating Hadoop workloads to Amazon EMR accelerates big data analytics modernization, increases productivity, and reduces operational cost. Refactoring coupled compute and storage to a decoupling architecture is a modern data solution. Jiseong Kim is a Senior Data Architect at AWS ProServe.

Cost-Benefit 52

Cost-Benefit Data Lake Dashboards Big Data 52

AWS Big Data

MARCH 3, 2023

From detailed design to a beta release, Tricentis had customers expecting to consume data from a data lake specific to only their data, and all of the data that had been generated for over a decade. Data export As stated earlier, some customers want to get an export of their test data and create their data lake.

Software 52

Software Data Lake Testing Cost-Benefit 52

IBM Big Data Hub

MARCH 14, 2024

Whether it’s for ad hoc analytics, data transformation, data sharing, data lake modernization or ML and gen AI, you have the flexibility to choose. With watsonx.data, customers can optimize price performance by selecting the most suitable open query engine for their specific workload needs.

Cost-Benefit 75

Cost-Benefit Metadata Optimization Management 75

AWS Big Data

JULY 10, 2024

This allows data analysts and data scientists to rapidly construct the necessary data preparation steps to meet their business needs. We use the new data preparation authoring capabilities to create recipes that meet our specific business needs for data transformations.

Interactive 116

Interactive Data Integration Visualization Statistics 116

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

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 115

Data Lake Data Warehouse Data-driven B2B 115

CIO Business Intelligence

AUGUST 3, 2023

When global technology company Lenovo started utilizing data analytics, they helped identify a new market niche for its gaming laptops, and powered remote diagnostics so their customers got the most from their servers and other devices. Without those templates, it’s hard to add such information after the fact.”

Analytics 98

Analytics Data Lake Metadata Cost-Benefit 98

AWS Big Data

DECEMBER 4, 2024

These nodes can implement analytical platforms like data lake houses, data warehouses, or data marts, all united by producing data products. Her special areas of interest are data analytics, machine learning/AI, and application modernization.

Metadata 105

Metadata Data Governance Data Quality Data-driven 105

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

Ontotext

FEBRUARY 12, 2024

For many organizations, a centralized data platform will fall short as it gives data teams much less autonomy over managing increasingly diverse and voluminous datasets. A centralized data engineering team focuses on building a governed self-serviced infrastructure, while domain teams use the services to build full-stack data products.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

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. Prior to his current role, he was VP of Analytics at AWS, where he worked across the entire AWS database portfolio.

Dashboards 80

Dashboards Data-driven Data Integration Data Lake 80

AWS Big Data

JANUARY 29, 2025

times more performant than Apache Spark 3.5.1), and ease of Amazon EMR with the control and proximity of your data center, empowering enterprises to meet stringent regulatory and operational requirements while unlocking new data processing possibilities. This method is ideal for recurring tasks or large-scale data transformations.

Big Data 91

Big Data Data Analytics Analytics Interactive 91

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 80

Snapshot Optimization Data Lake Metadata 80

AWS Big Data

NOVEMBER 6, 2024

Second, because traditional data warehousing approaches are unable to keep up with the volume, velocity, and variety of data, engineering teams are building data lakes and adopting open data formats such as Parquet and Apache Iceberg to store their data. For Source , select Direct PUT.

Metadata 116

Metadata Data Lake Management Internet of Things 116

Jet Global

MAY 9, 2024

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. 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

OCTOBER 1, 2024

Trino allows users to run ad hoc queries across massive datasets, making real-time decision-making a reality without needing extensive data transformations. This is particularly valuable for teams that require instant answers from their data. Data Lake Analytics: Trino doesn’t just stop at databases.

Dashboards 52

Dashboards Data Lake Reporting Cost-Benefit 52