AWS Big Data

AUGUST 9, 2024

We carried out the migration as follows: We created a new cluster with eight ra3.4xlarge nodes from the snapshot of our four-node dc2.8xlarge cluster. TB of data. We turned off our internal ETL and ELT orchestrator, to prevent our data from being updated during the migration period.

Data Lake 107

Data Lake Analytics Data Warehouse Data-driven 107

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 93

Data Lake Analytics Snapshot Data Quality 93

Cloudera

AUGUST 3, 2022

The introduction of “Secure Access” mode to HWC avoids these drawbacks by relying on Hive to obtain a secure snapshot of the data that is then operated upon by Spark. If you are already a user of HWC, you can continue using hive.executeQuery() or hive.sql() in your Spark application to obtain the data securely. . df.show().

Snapshot 90

Snapshot Cost-Benefit Machine Learning Data Science 90

AWS Big Data

JULY 26, 2023

Every time the business requirement changes (such as adding data sources or changing data transformation logic), you make changes on the AWS Glue app stack and re-provision the stack to reflect your changes. rename_field('id', 'org_id').rename_field('name',

Data Integration 79

Data Integration Snapshot Testing Visualization 79

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 116

Data Lake Management Metrics Data Warehouse 116

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 117

Management Metadata Analytics Dashboards 117

AWS Big Data

JANUARY 17, 2024

The following are some highlighted steps: Run a snapshot query. %%sql You also can use transactional data lake features such as running snapshot queries, incremental queries, time travel, and DML query. Melody Yang is a Senior Big Data Solution Architect for Amazon EMR at AWS. You can now follow the steps in the notebook.

Data Lake 107

Data Lake Snapshot Big Data Data-driven 107

AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. Data engineers are crucial for schema conversion and data transformation, and DBAs can handle cluster configuration and workload monitoring. Platform architects define a well-architected platform.

Data Warehouse 99

Data Warehouse KPI Optimization Cost-Benefit 99

AWS Big Data

MARCH 16, 2023

A source of unpredictable workloads is dbt Cloud , which SafetyCulture uses to manage data transformations in the form of models. SafetyCulture also successfully ran its dbt project with all seeds, models, and snapshots materialized into the serverless instance via run commands from the dbt Cloud IDE and dbt Cloud CI jobs.

Data Warehouse 52

Data Warehouse Testing Snapshot Modeling 52

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 90

Data Lake Dashboards Metrics Metadata 90

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 66

Data Lake Metadata Business Analysis Data-driven 66

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

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 76

Snapshot Optimization Data Lake Metadata 76

AWS Big Data

NOVEMBER 6, 2024

For example, you can write some records using a batch ETL Spark job and other data from a Flink application at the same time and into the same table. Third, it allows scenarios such as time travel and rollback, so you can run SQL queries on a point-in-time snapshot of your data, or rollback data to a previously known good version.

Metadata 109

Metadata Data Lake Management Internet of Things 109

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