AWS Big Data

JANUARY 17, 2024

Another popular transaction data lake use case is incremental query. 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 following are some highlighted steps: Run a snapshot query. %%sql

Data Lake 98

Data Lake Snapshot Big Data Data-driven 98

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 109

Management Metadata Analytics Dashboards 109

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. Let’s refer to this S3 bucket as the raw layer. Data transformation – Steps 3 and 4 represent an EMR Serverless Spark application (Amazon EMR 6.9

Data Lake 84

Data Lake Dashboards Metrics Metadata 84

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 108

Data Lake Management Metrics Data Warehouse 108

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 62

Data Lake Metadata Business Analysis Data-driven 62

DataKitchen

JANUARY 23, 2023

It is important to have additional tools and processes in place to understand the impact of data errors and to minimize their effect on the data pipeline and downstream systems. These operations can include data movement, validation, cleaning, transformation, aggregation, analysis, and more.

Testing 130

Testing Data Governance Data Quality Data-driven 130