Cloudera

DECEMBER 9, 2022

Developers need to onboard new data sources, chain multiple data transformation steps together, and explore data as it travels through the flow. A reimagined visual editor to boost developer productivity and enable self service. Figure 5: Parameter references in the configuration panel and auto-complete.

Testing 97

Testing Cost-Benefit Interactive Visualization 97

AWS Big Data

FEBRUARY 26, 2024

Additionally, you can configure OpenSearch Ingestion to apply data transformations before delivery. The content includes a reference architecture, a step-by-step guide on infrastructure setup, sample code for implementing the solution within a use case, and an AWS Cloud Development Kit (AWS CDK) application for deployment.

Dashboards 62

Dashboards Testing Metrics Optimization 62

Jet Global

FEBRUARY 23, 2024

Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation. Data mapping is important for several reasons.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

AWS Big Data

JULY 31, 2023

AWS DMS enables us to capture deltas, including deletes from the source database, through the use of Change Data Capture (CDC) configuration. CDC in DMS enables us to capture deltas without writing code and without missing any changes, which is critical for the integrity of the data. Navigate to the Visual tab. Choose Confirm.

Sales 52

Sales Data Warehouse Visualization Testing 52

AWS Big Data

JUNE 29, 2023

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. Notebooks are provisioned quickly and provide a way for you to instantly view and analyze your streaming data.

Data Analytics 73

Data Analytics Analytics IoT Data Lake 73

AWS Big Data

FEBRUARY 8, 2023

For addressing data quality challenges in Amazon Simple Storage Service (Amazon S3) data lakes and data pipelines, AWS has announced AWS Glue Data Quality (preview). Create a table for weight information This reference table holds two columns; the table name and the column mapping with weights.

Data Quality 74

Data Quality Testing Data Warehouse Unstructured Data 74

AWS Big Data

FEBRUARY 6, 2023

In this post, we dive deep into the tool, walking through all steps from log ingestion, transformation, visualization, and architecture design to calculate TCO. For more details on how to configure and schedule the log collector, refer to the yarn-log-collector GitHub repo.

Dashboards 73

Dashboards Optimization Data Lake Cost-Benefit 73

AWS Big Data

OCTOBER 31, 2023

citibike-tripdata-destination-ACCOUNT_ID – The bucket used for storing the transformed dataset. When implementing the solution in this post, replace references to airflow-blog-bucket-ACCOUNT_ID and citibike-tripdata-destination-ACCOUNT_ID with the names of your own S3 buckets. Run the DAG Let’s look at how to run the DAGs.

Data Processing 89

Data Processing Management Publishing Testing 89

AWS Big Data

MARCH 13, 2024

You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches. You can test this solution yourself using the AWS Samples GitHub repository. This method uses GZIP compression to optimize storage consumption and query performance.

Analytics 94

Analytics IoT Metadata Internet of Things 94

AWS Big Data

JULY 6, 2023

It has not been specifically designed for heavy data transformation tasks. Now that the data is on Amazon S3, you can delete the directory that has been downloaded from your Linux machine. Create the Lambda functions For step-by-step instructions on how to create a Lambda function, refer to Getting started with Lambda.

Machine Learning 126

Machine Learning Visualization IoT Digital Transformation 126

AWS Big Data

APRIL 25, 2024

Alternatively, you can use AWS Glue for Apache Spark, which provides built-in support for bucketing configurations during the data transformation process. AWS Glue allows you to define bucketing parameters, such as the number of buckets and the columns to bucket on, providing an optimized data layout for efficient querying with Athena.

Optimization 67

Optimization Data Lake Cost-Benefit Reporting 67

AWS Big Data

APRIL 25, 2023

The following AWS services are used for data ingestion, processing, and load: Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications like Salesforce, SAP, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift , in just a few clicks.

Sales 69

Sales Visualization Software Marketing 69

Jet Global

MAY 1, 2023

This is in contrast to traditional BI, which extracts insight from data outside of the app. that gathers data from many sources. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Domino Data Lab

AUGUST 20, 2019

We are going to turn our attention away from expanding our catalog of models [as mentioned previously in the book ] and instead take a closer look at the data. Feature engineering refers to manipulation—addition, deletion, combination, mutation—of the features. Separate out a hold-out test set. Don’t peek at it.

Testing 68

Testing Modeling Interactive Measurement 68

Occam's Razor

OCTOBER 19, 2010

so you have some reference as to where each item fits (and this will also make it easier for you to pick tools for the priority order referenced in Context #3 above). If you can show ROI on a DW it would be a good use of your money to go with Omniture Discover, WebTrends Data Mart, Coremetrics Explore. and embrace Multiplicity.

Analytics 135

Analytics Testing Measurement Optimization 135

Alation

JANUARY 17, 2023

A modern data stack relies on cloud computing, whereas a legacy data stack stores data on servers instead of in the cloud. Modern data stacks provide access for more data professionals than a legacy data stack. An example of a data science tool is Dataiku. How Can I Build a Modern Data Stack?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

O'Reilly on Data

JANUARY 17, 2023

” I, thankfully, learned this early in my career, at a time when I could still refer to myself as a software developer. Upload your data, click through a workflow, walk away. If you’re a professional data scientist, you already have the knowledge and skills to test these models. It does not exist in the code.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325