AWS Big Data

APRIL 27, 2023

It supports modern analytical data lake operations such as create table as select (CTAS), upsert and merge, and time travel queries. Athena also supports the ability to create views and perform VACUUM (snapshot expiration) on Apache Iceberg tables to optimize storage and performance.

Data Lake 73

Data Lake Snapshot Optimization Data Transformation 73

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 87

Data Lake Analytics Snapshot Optimization 87

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 104

Management Metadata Analytics Dashboards 104

O'Reilly on Data

OCTOBER 19, 2021

To manage the dynamism, we can resort to taking snapshots that represent immutable points in time: of models, of data, of code, and of internal state. Enter the software development layers. Versioning. ML app and software artifacts exist and evolve in a dynamic environment. For this reason, we require a strong versioning layer.

IT 346

IT Testing Experimentation Software 346

AWS Big Data

MARCH 3, 2023

Any time new test cases or test results are created or modified, events trigger such that processing is immediate and new snapshot files are available via an API or data is pulled at the refresh frequency of the reporting or business intelligence (BI) tool. Fixed-size data files avoid further latency due to unbound file sizes.

Software 52

Software Data Lake Testing Cost-Benefit 52

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 83

Data Lake Dashboards Metrics Metadata 83

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 102

Data Lake Management Metrics Data Warehouse 102

AWS Big Data

JANUARY 27, 2023

The Amazon EMR Flink CDC connector reads the binlog data and processes the data. Transformed data can be stored in Amazon S3. We use the AWS Glue Data Catalog to store the metadata such as table schema and table location. Continue the subsequent steps to complete your EMR cluster creation.

Data Lake 62

Data Lake Metadata Business Analysis Data-driven 62

DataKitchen

JANUARY 23, 2023

To capture a more complete picture of the data’s journey, it is important to have a DataOps Observability system in place. Data lineage is static and often lags by weeks or months. Data lineage is often considered static because it is typically based on snapshots of data and metadata taken at a specific time.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

NOVEMBER 7, 2023

Advantages : Replication reduces the load on source systems because data extraction occurs at predefined intervals, reducing the real-time impact on production systems. It provides consistency in data for reporting purposes, as you are working with snapshots of the data at a particular point in time.

Enterprise 52

Enterprise Data Warehouse Operational Reporting Reporting 52