AWS Big Data

MARCH 27, 2024

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. Complete the implementation tasks such as data ingestion and performance testing.

Testing 101

Testing Data Warehouse Metrics Cost-Benefit 101

AWS Big Data

MARCH 12, 2024

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

Data Quality 92

Data Quality Measurement Testing Visualization 92

AWS Big Data

AUGUST 22, 2023

You can create an AWS Cloud9 environment in one of the private subnets available in your AWS account to set up test data in Amazon RDS. Prerequisites For instructions to set up your environment for implementing the solution proposed in this post, refer to Deploy the application in the GitHub repo. modules, respectively.

Data Processing 90

Data Processing Testing Data Lake Data Integration 90

AWS Big Data

APRIL 3, 2023

Tens of thousands of customers run business-critical workloads on Amazon Redshift , AWS’s fast, petabyte-scale cloud data warehouse delivering the best price-performance. With Amazon Redshift, you can query data across your data warehouse, operational data stores, and data lake using standard SQL.

Data Warehouse 99

Data Warehouse Testing Data Lake Data-driven 99

AWS Big Data

MAY 18, 2023

You can discover and connect to over 70 diverse data sources, manage your data in a centralized data catalog, and create, run, and monitor data integration pipelines to load data into your data lakes and your data warehouses. For more details, refer to Spark Release 3.3.0 runtime ( 3.5

Testing 79

Testing Data Lake Cost-Benefit Data Integration 79

AWS Big Data

MARCH 6, 2024

For an overview of how to build an ACID compliant data lake using Iceberg, refer to Build a high-performance, ACID compliant, evolving data lake using Apache Iceberg on Amazon EMR. Test the batch solution In the CloudFormation template deployed using the deploy_1.sh AWS Glue, and Athena.

Metrics 96

Metrics Statistics Testing Data Lake 96

AWS Big Data

APRIL 19, 2023

To configure AWS CLI interaction with AWS, refer to Quick setup. json ) to DynamoDB (for more information, refer to Write data to a table using the console or AWS CLI ): { "name": "step1.q", json ) to DynamoDB (for more information, refer to Write data to a table using the console or AWS CLI ): { "name": "step1.q",

Metadata 82

Metadata Testing Data Lake Consulting 82

AWS Big Data

JUNE 6, 2023

These are six main steps in the data pipeline: Amazon EventBridge triggers an AWS Lambda function when the event pattern for AWS Glue Data Quality matches the defined rule. For more information, refer to Working with Query Results, Output Files, and Query History. For S3 path , enter the S3 path to your data source. (

Data Quality 74

Data Quality Metrics Visualization Dashboards 74

AWS Big Data

JANUARY 30, 2023

Many customers need an ACID transaction (atomic, consistent, isolated, durable) data lake that can log change data capture (CDC) from operational data sources. There is also demand for merging real-time data into batch data. Delta Lake framework provides these two capabilities.

Insurance 82

Insurance Data Lake Data-driven Management 82

AWS Big Data

NOVEMBER 17, 2023

Athena provides a simplified, flexible way to analyze petabytes of data where it lives. You can analyze data or build applications from an Amazon Simple Storage Service (Amazon S3) data lake and 30 data sources, including on-premises data sources or other cloud systems using SQL or Python.

Optimization 115

Optimization Statistics Metadata Data Lake 115

AWS Big Data

MARCH 28, 2023

As organizations across the globe are modernizing their data platforms with data lakes on Amazon Simple Storage Service (Amazon S3), handling SCDs in data lakes can be challenging.

Data Lake 73

Data Lake Testing Snapshot Sales 73

AWS Big Data

JUNE 29, 2023

Amazon Kinesis Data Analytics makes it easy to transform and analyze streaming data in real time. In this post, we discuss why AWS recommends moving from Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics for Apache Flink to take advantage of Apache Flink’s advanced streaming capabilities.

Data Analytics 76

Data Analytics Analytics IoT Data Lake 76

AWS Big Data

MAY 30, 2023

Customers have been using data warehousing solutions to perform their traditional analytics tasks. Traditional batch ingestion and processing pipelines that involve operations such as data cleaning and joining with reference data are straightforward to create and cost-efficient to maintain.

Data Lake 83

Data Lake Data Analytics Analytics Data Processing 83

AWS Big Data

MARCH 27, 2024

These services enable you to collect and analyze data in near real time and put a comprehensive data governance framework in place that uses granular access control to secure sensitive data from unauthorized users. To create an AWS HealthLake data store, refer to Getting started with AWS HealthLake.

Data Analytics 94

Data Analytics Analytics Data Warehouse Data Lake 94

AWS Big Data

DECEMBER 13, 2023

Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. To see how to manage redshift cluster security group, refer Managing VPC security groups for a cluster.

Data Warehouse 100

Data Warehouse Snapshot Data Processing Management 100

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 115

Data Lake Metadata Optimization Statistics 115

AWS Big Data

NOVEMBER 6, 2023

You can attach an EMR Studio Workspace to an EMR cluster, and use the compute power of the EMR cluster and run data science jobs on the cluster. Data is often stored in data lakes managed by AWS Lake Formation , enabling you to apply fine-grained access control through a simple grant or revoke mechanism.

Data Lake 97

Data Lake Sales Management Testing 97

AWS Big Data

APRIL 26, 2024

Use Lake Formation to grant permissions to users to access data. Test the solution by accessing data with a corporate identity. Audit user data access. For a complete guide on creating and providing a certificate, refer to Providing certificates for encrypting data in transit with Amazon EMR encryption.

Analytics 97

Analytics Data Lake Management Enterprise 97

AWS Big Data

NOVEMBER 13, 2023

Amazon Redshift is a fully managed data warehousing service that offers both provisioned and serverless options, making it more efficient to run and scale analytics without having to manage your data warehouse. These upstream data sources constitute the data producer components.

Data Warehouse 94

Data Warehouse Data Lake Analytics Data Science 94

AWS Big Data

MAY 16, 2023

Solution overview The AWS Data Lab offers accelerated, joint engineering engagements between customers and AWS technical resources to create tangible deliverables that accelerate data, analytics, artificial intelligence (AI), machine learning (ML), serverless, and container modernization initiatives.

Data Lake 71

Data Lake Cost-Benefit Optimization Testing 71

AWS Big Data

MAY 28, 2024

The details of each step are as follows: Populate the Amazon Redshift Serverless data warehouse with company stock information stored in Amazon Simple Storage Service (Amazon S3). Redshift Serverless is a fully functional data warehouse holding data tables maintained in real time. This is testing for hallucination.

Unstructured Data 66

Unstructured Data Structured Data Data Warehouse Testing 66

DataKitchen

OCTOBER 11, 2021

The biggest challenge is broken data pipelines due to highly manual processes. Figure 1 shows a manually executed data analytics pipeline. First, a business analyst consolidates data from some public websites, an SFTP server and some downloaded email attachments, all into Excel.

Testing 152

Testing Metadata Dashboards Statistics 152

AWS Big Data

JUNE 12, 2023

Organizations across the world are increasingly relying on streaming data, and there is a growing need for real-time data analytics, considering the growing velocity and volume of data being collected. Refer appendix section for more information on this feature. Refer to the first stack’s output.

Management 82

Management Metadata Testing Internet of Things 82

AWS Big Data

DECEMBER 18, 2023

Solution overview One of the common functionalities involved in data pipelines is extracting data from multiple data sources and exporting it to a data lake or synchronizing the data to another database. For more information, refer to IAM Policies for invoking AWS Glue job from Step Functions.

Metadata 123

Metadata Visualization Data Lake Data-driven 123

AWS Big Data

OCTOBER 23, 2023

Modern applications store massive amounts of data on Amazon Simple Storage Service (Amazon S3) data lakes, providing cost-effective and highly durable storage, and allowing you to run analytics and machine learning (ML) from your data lake to generate insights on your data.

Data Lake 104

Data Lake Visualization Optimization Interactive 104

AWS Big Data

AUGUST 16, 2023

Apache Iceberg is an open table format for very large analytic datasets. Iceberg manages large collections of files as tables, and it supports modern analytical data lake operations such as record-level insert, update, delete, and time travel queries. On the Code tab, choose Test , then Configure test event.

Data Lake 74

Data Lake Metadata Testing Snapshot 74

AWS Big Data

JULY 12, 2023

Amazon Redshift helps you break down the data silos and allows you to run unified, self-service, real-time, and predictive analytics on all data across your operational databases, data lake, data warehouse, and third-party datasets with built-in governance. This is often a laborious and error-prone process.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

Cloudera

JULY 9, 2018

Some call it the “golden triangle,” but in this blog, we refer to it as the iron triangle. The Turing test is all about how your algorithm simulates human behaviors. A successful Turing test means that your algorithm is very human-like. Business intelligence is the glue that binds traditional BI and data science together.

Machine Learning 42

Machine Learning Data Science Testing Visualization 42

Cloudera

DECEMBER 12, 2018

Now you can just land those files and instead of having a separate filer that doesn’t have any compute processing in it, you can land them in a distributed file system like HDFS, which is generally co-located with a data processing engine like Impala. Greg Rahn: I refer to this as friction-free data landing. But, yeah.

Data Warehouse 42

Data Warehouse Marketing Big Data Data Lake 42

AWS Big Data

JUNE 15, 2023

Amazon EMR Notebooks , a managed environment based on Jupyter and JupyterLab notebooks, enables you to interactively analyze and visualize data, collaborate with peers, and build applications using EMR clusters running Apache Spark. For more information about SAS, refer to Delegate access by using a shared access signature. and 6.10.0.

Data Lake 89

Data Lake Big Data Management Testing 89

Andrew White

JANUARY 11, 2021

It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. It is meant to be a desk-reference for that role for 2021. Does Data warehouse as a software tool will play role in future of Data & Analytics strategy?

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52