Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Power Query provides a user-friendly solution for connecting, extracting, transforming, and loading data from a variety of sources. Acting as a robust engine for data preparation and transformation, Power Query features a graphical interface that simplifies the data retrieval process and includes a Power Query Editor for implementing necessary changes. The versatility of the engine allows it to be integrated across numerous products and services, meaning the storage location of the data is determined by the specific application of Power Query. This tool enables users to efficiently carry out the extract, transform, and load (ETL) processes for their data needs. With Microsoft’s Data Connectivity and Data Preparation technology, users can easily access and manipulate data from hundreds of sources in a straightforward, no-code environment. Power Query is equipped with support for a multitude of data sources through built-in connectors, generic interfaces like REST APIs, ODBC, OLE, DB, and OData, and even offers a Power Query SDK for creating custom connectors tailored to individual requirements. This flexibility makes Power Query an indispensable asset for data professionals seeking to streamline their workflows.
Description
Saras Daton is a specialized ETL/ELT platform designed for omnichannel and ecommerce enterprises, allowing teams to consolidate data from various fragmented sources into centralized cloud data warehouses. With over 200 pre-built connectors—some tailored for long-tail ecommerce platforms—it facilitates custom transformations that accommodate order histories, inventory updates, marketing performance metrics, and other intricate requirements of commerce. The platform efficiently manages incremental extraction, schema mapping, historical data loads, and routine incremental updates, providing both batch and real-time processing capabilities. It incorporates parallel processing to handle large volumes of data, alongside checkpoints and retries to ensure the reliability of data pipelines. Users can take advantage of table-level scheduling features for multi-speed pipelines, while CRON scheduling allows for tailored replication frequencies, enabling supported sources to achieve replication intervals as short as 15 minutes. Furthermore, granular loading and schema management provide users the ability to oversee data at both the table and column levels, utilizing methods such as append, upsert, or truncate-and-load to optimize their data ingestion processes. This comprehensive approach ensures that ecommerce businesses can handle their data needs effectively and efficiently.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Amazon Redshift
Yes
Google Analytics
Yes
Mixpanel
Yes
MySQL
Yes
Snowflake
Yes
Zendesk
Yes
Acterys
Yes
Amazon DSP
No
AtScale
Yes
Azure DevOps Server
Yes
Integrations
Amazon Redshift
Yes
Google Analytics
Yes
Mixpanel
Yes
MySQL
Yes
Snowflake
Yes
Zendesk
Yes
Acterys
No
Amazon DSP
Yes
AtScale
No
Azure DevOps Server
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$1,999 per month
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
powerquery.microsoft.com/en-us/
Vendor Details
Company Name
Saras Analytics
Founded
2016
Country
United States
Website
www.sarasanalytics.com/saras-daton
Product Features
Data Preparation
Collaboration Tools
No
Data Access
No
Data Blending
No
Data Cleansing
No
Data Governance
No
Data Mashup
No
Data Modeling
No
Data Transformation
No
Machine Learning
No
Visual User Interface
No
ETL
Data Analysis
No
Data Filtering
No
Data Quality Control
No
Job Scheduling
No
Match & Merge
No
Metadata Management
No
Non-Relational Transformations
No
Version Control
No
Product Features
ETL
Data Analysis
No
Data Filtering
No
Data Quality Control
No
Job Scheduling
No
Match & Merge
No
Metadata Management
No
Non-Relational Transformations
No
Version Control
No