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 Big Data Metrics 98

DataKitchen

NOVEMBER 10, 2023

The Perilous State of Today’s Data Environments Data teams often navigate a labyrinth of chaos within their databases. Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team. Identifying Anomalies: Use advanced algorithms to detect anomalies in data patterns.

Data Quality 136

Data Quality Testing Data Lake Data Integration 136

AWS Big Data

AUGUST 9, 2024

These processes retrieve data from around 90 different data sources, resulting in updating roughly 2,000 tables in the data warehouse and 3,000 external tables in Parquet format, accessed through Amazon Redshift Spectrum and a data lake on Amazon Simple Storage Service (Amazon S3). We started with 115 dc2.large

Data Lake 111

Data Lake Analytics Data Warehouse Data-driven 111

DataKitchen

JULY 27, 2023

Here are a few examples that we have seen of how this can be done: Batch ETL with Azure Data Factory and Azure Databricks: In this pattern, Azure Data Factory is used to orchestrate and schedule batch ETL processes. Azure Blob Storage serves as the data lake to store raw data. Azure Machine Learning). So go ahead.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

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

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. For instance, one enhancement involves integrating cross-functional squads to support data literacy.

Metadata 105

Metadata Data Governance Data Quality Data-driven 105

CIO Business Intelligence

APRIL 8, 2025

Comparison of modern data architectures : Architecture Definition Strengths Weaknesses Best used when Data warehouse Centralized, structured and curated data repository. Inflexible schema, poor for unstructured or real-time data. Data lake Raw storage for all types of structured and unstructured data.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. The success criteria are the key performance indicators (KPIs) for each component of the data workflow. This requires a dedicated team of 3–7 members building a serverless data lake for all data sources.

Data Warehouse 115

Data Warehouse KPI Optimization Cost-Benefit 115

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

APRIL 11, 2023

With the proliferation of IoT devices and the abundance of data generated by them, it has become possible to collect real-time data on inventory levels, customer behavior, and other key metrics. In the inventory management and forecasting solution, AWS Glue is recommended for data transformation.

Forecasting 98

Forecasting Management IoT Data-driven 98

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 126

Data Warehouse Testing Data Quality Reporting 126

AWS Big Data

FEBRUARY 6, 2023

Let’s look at some key metrics. After analyzing YARN logs by various metrics, you’re ready to design future EMR architectures. He has a specialty in big data services and technologies and an interest in building customer business outcomes together. Jiseong Kim is a Senior Data Architect at AWS ProServe.

Dashboards 98

Dashboards Optimization Data Lake Cost-Benefit 98

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 97

Reporting Metrics Optimization Data Lake 97

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

AUGUST 19, 2024

This allows business analysts and decision-makers to gain valuable insights, visualize key metrics, and explore the data in depth, enabling informed decision-making and strategic planning for pricing and promotional strategies. Refer to Editing AWS Glue managed data transform nodes for more information.

Analytics 119

Analytics Data-driven Data Integration Data Lake 119

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. They can now analyze business metrics in near-real time and make data-driven decisions faster than ever before.

Dashboards 80

Dashboards Data-driven Data Integration Data Lake 80

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

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

AWS Big Data

MARCH 3, 2023

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. Fixed-size data files avoid further latency due to unbound file sizes.

Software 52

Software Data Lake Testing Cost-Benefit 52

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

AWS Big Data

APRIL 29, 2025

While enabling organization-wide efficiency, the team also applied these principles to the data architecture, making sure that CLEA itself operates frugally. After evaluating various tools, we built a serverless data transformation pipeline using Amazon Athena and dbt. The Source stage maintains raw data in its original form.

Data Transformation 55

Data Transformation Cost-Benefit Testing Data Lake 55

AWS Big Data

MAY 12, 2025

Streaming pipelines used Spark Streaming to ingest real-time data from Kafka, writing raw datasets to an Amazon Simple Storage Service (Amazon S3) data lake while simultaneously loading them into BigQuery and Google Cloud Storage to build logical data layers.

Metrics 85

Metrics Cost-Benefit Metadata Data Lake 85

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. Choose your web ACL.

Snapshot 82

Snapshot Optimization Data Lake Metadata 82