AWS Big Data

OCTOBER 11, 2023

Organizations with legacy, on-premises, near-real-time analytics solutions typically rely on self-managed relational databases as their data store for analytics workloads. Near-real-time streaming analytics captures the value of operational data and metrics to provide new insights to create business opportunities.

Management 109

Management Metadata Analytics Dashboards 109

AWS Big Data

APRIL 27, 2023

Apache Iceberg is an open table format for data lakes that manages large collections of files as tables. It supports modern analytical data lake operations such as create table as select (CTAS), upsert and merge, and time travel queries. The following diagram illustrates the solution architecture.

Data Lake 75

Data Lake Snapshot Optimization Data Transformation 75

O'Reilly on Data

OCTOBER 19, 2021

Since software engineers manage to build ordinary software without experiencing as much pain as their counterparts in the ML department, it begs the question: should we just start treating ML projects as software engineering projects as usual, maybe educating ML practitioners about the existing best practices? Orchestration. Versioning.

IT 346

IT Testing Experimentation Software 346

AWS Big Data

JULY 20, 2023

Moreover, running advanced analytics and ML on disparate data sources proved challenging. To overcome these issues, Orca decided to build a data lake. By decoupling storage and compute, data lakes promote cost-effective storage and processing of big data. This ensures that the data is suitable for training purposes.

Data Lake 89

Data Lake Analytics Snapshot Optimization 89

AWS Big Data

MARCH 3, 2023

In this post, we share how the AWS Data Lab helped Tricentis to improve their software as a service (SaaS) Tricentis Analytics platform with insights powered by Amazon Redshift. Although Tricentis has amassed such data over a decade, the data remains untapped for valuable insights.

Software 52

Software Data Lake Testing Cost-Benefit 52

AWS Big Data

MARCH 3, 2023

You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes. 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 Lake 85

Data Lake Dashboards Metrics Metadata 85

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 107

Data Lake Management Metrics Data Warehouse 107

AWS Big Data

JANUARY 27, 2023

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. Apache Flink is a widely used data processing engine for scalable streaming ETL, analytics, and event-driven applications. Apache Hudi also has its own catalog management.

Data Lake 63

Data Lake Metadata Business Analysis Data-driven 63

DataKitchen

JANUARY 23, 2023

Data lineage and a data catalog are better together because they provide a more complete and accurate view of the data. Data lineage provides information about the origin, history, and movement of data, while runtime operations provide information about the actions performed on data while it is being processed.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

NOVEMBER 7, 2023

Add in the de facto requirement to combine all your reporting data and it presents quite a challenge. As more companies move their data into the cloud, methods for storing and managing that data also adapt and grow. This growth is caused, in part, by the increasing use of cloud platforms for data storage and processing.

Enterprise 52

Enterprise Data Warehouse Operational Reporting Reporting 52