Xplenty vs. K3: ETL software comparison

K3 Takes Xplenty to the Mat

Enterprise-scale data-dependent organizations will prefer K3 to Xplenty for several reasons:

Flexibility – Xplenty can be deployed via a hosted cloud. K3 is comfortable working on hosted or private clouds and on-premises environments and flows data seamlessly to and from platforms living in either space.
Error handling – Xplenty’s error reporting offers little more than a text-dump log decipherable only by tech-savvy types. K3 isolates error conditions to prevent it from failing the process. When failure cannot be averted, the platform initiates notifications through messages and/or if/then procedures.
Auditing – The absence of an audit-trail means Xplenty cannot effectively detect missing or anomalous data. K3’s intuitive ETL data audit function uncovers not only “hard” errors but also illogical entries, incomplete datasets, and other inconsistencies.
Data transformation – Xplenty performs little more than data synchronization and several basic filtering and sorting preload operations. K3 harnesses change data capture to transform only new or altered data to minimize calculation time.

WHAT THIS MEANS FOR YOU:

K3 ensures data integrity by finding and managing not only data errors but also output anomalies.

K3 allows organizations to integrate, store, and compute data, not merely format it, all within the platform.

With K3, you can retrieve any previous versions to compare and troubleshoot code, content, documentation, or record.

K3 permits transformations in Python, Java and GUI. Xplenty requires workarounds coded exclusively in SQL.

Platform Review K3 and Xplenty

Xplenty is a cloud-exclusive ETL data pipeline provider that automates data flows across multiple sources and destinations. Its strength lies in its ability to connect to a wide range of SaaS platforms, but its data transformation capabilities are rudimentary.

K3 is deployable on premise as well as across cloud servers, where it performs sophisticated data prep and transformation of conventional and nonconforming data to make it compatible with virtually every database, data lake, operational application and visualization devise enterprises use.

Feature Comparison of K3 v/s Xplenty

Data Deployment

Hosted Cloud Deployment

Yes

Private Cloud Deployment

Yes

No

On Premise Deployment

Yes

No

Data Stays Private Guarantee

Yes

No

Scalability

No Per User Charges

Yes

Database CDC Connections (see databases)

Yes

No

SaaS Connections (see connectors)

Yes

Multiple Formats (XML, JSON, EDI, etc.)

Yes

No

Features Comparison

Data Rules Engines

Yes

No

Low Code Data Route Configuration

Yes

Low Code ETL

Yes

Some

Data Orchestration

Yes

No

Low Learning Curve

Yes

Audit Trail

Yes

No

Test/Prod accounts included

Yes

No

Detailed Analysis

K3

Xplenty

Error Reporting

K3 decides not only how data should appear and perform at the end of each step in the ETL process, but also the effects manipulation and formatting will exert on the raw and combined data. The K3 approach to data errors is fourfold: 1. Prevent them from occurring. 2. When errors occur in certain data sections, proceed in transforming and loading unaffected data. 3. Repair the errant data, transform and reload it. 4. If steps 1-3 do not solve the problem, initiate the proper notifications and reporting.

Xplenty’s error reporting log and correction workflow is not especially helpful. Often counterintuitive, the reports contain extraneous information that users must wade through and evaluation on their way to the good stuff. Often this requires the assistance of IT staff, negating the advantages of the low-code environment. The limited insights provided by error messages creates challenges for rectifying data processes and is exacerbated by the lack of audit trails. Users cannot compare iterations or tweak older packages to test fixes without rerunning the entire package.

Auditing

Going beyond error management, K3’s data auditing component rectifies transformation-rule inconsistencies and output anomalies by mapping, iterating, and documenting all changes in data’s appearance and behavior as it moves along the pipeline and commutes among sources, applications, and storage facilities. Unlike Xplenty, K3 gives users dynamic insight into why and how data changes as it is being transformed.

Like too many data pipeline vendors, Xplenty seems to consider data audits as a “nice to have” rather than a critical part of the data integrity technology stack. As a result, Xplenty essentially takes a hard pass on incorporating audit trails into its ETL workflow. This exposes internal stakeholders and outside partners to uncertainty over whether transformations are delivering correct data formatting and performance.

Transformation

K3 offers multiple tools for analyzing transformed data delivered to databases.Using change data capture, K3 is able to transform only data that has been updated or altered since the previous query and load this smaller set into downstream apps, saving time, computational resources, and money.

Perhaps Xplenty doesn’t bother with transformation auditing because it performs so little of it. The platform’s primitive data transformation engine forces users to develop SQL workarounds which can lead to lost, improperly formatted, and corrupted data.

K3’s Winning Transformation

Data-driven organizations need fast, accurate, complete transformation of data from a variety of on-premise and cloud-based sources. They cannot afford to settle for a platform that offers only basic data prep and integration. K3 passes the test with flying colors.Xplenty integrates with SaaS platforms, but it fails to incorporate data orchestration capabilities that could optimize performance.

Upstream

Downstream