AWS Big Data

OCTOBER 11, 2023

Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources. Data transformations through stored procedures and use of materialized views to curate datasets and generate insights is a known pattern with relational databases.

Management 131

Management Metadata Analytics Dashboards 131

AWS Big Data

JULY 20, 2023

Specifically, the system uses Amazon SageMaker Processing jobs to process the data stored in the data lake, employing the AWS SDK for Pandas (previously known as AWS Wrangler) for various data transformation operations, including cleaning, normalization, and feature engineering.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

AWS Big Data

JANUARY 17, 2024

Incremental query refers to a query strategy that focuses on processing and analyzing only the new or updated data within a data lake since the last query. The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query.

Data Lake 116

Data Lake Snapshot Big Data Data-driven 116

AWS Big Data

MARCH 3, 2023

Data ingestion – Steps 1 and 2 use AWS DMS, which connects to the source database and moves full and incremental data (CDC) to Amazon S3 in Parquet format. Data transformation – Steps 3 and 4 represent an EMR Serverless Spark application (Amazon EMR 6.9 Let’s refer to this S3 bucket as the raw layer.

Data Lake 98

Data Lake Dashboards Metrics Metadata 98

AWS Big Data

JANUARY 27, 2023

With a unified data catalog, you can quickly search datasets and figure out data schema, data format, and location. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos. Refer to Catalogs for more information.

Data Lake 88

Data Lake Metadata Business Analysis Data-driven 88

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

AWS Big Data

FEBRUARY 18, 2025

These include managing complex extract, transform, and load (ETL) processes, handling schema validation, providing reliable delivery, and maintaining custom code for data transformations. Firehose delivers streaming data with configurable buffering options that can be optimized for near-zero latency.

Snapshot 80

Snapshot Optimization Data Lake Metadata 80