K3 vs Alooma: Which Has the Upper Hand in ETL?

Alooma and K3: Comparative Overview

Let’s see how Alooma matches up against K3 when it comes to key ETL workflow.

Deployment: K3 seamlessly integrates data across ETL platforms, data sources, deployment scenarios, and data storage platforms. looma limits users to hosted-cloud deployment and the Google data environment.
Data Load: As primarily a data pipeline service, Alooma’s strength lies in data extraction. It’s transformation tool lacks robustness, and its load function is slow. K3’s comprehensive ETL suite leverages change data capture to speed the transformation and loading of key inputs.
Data Rules Engine: K3’s ETL rules engine performs – with no code required – sophisticated data tests and scenario progressions to arrive at end-to-end management solutions. Alooma has no on-board rules engine, forcing users to investigate the details of each component and query to input and confirm data.

WHAT IT ALL MEANS:

Alooma only works with Google’s BigQuery data warehouse as a target.

K3’s connects to hundreds of downstream targets. K3’s no-code rules engine painlessly allows business analysts to create meaningful data rules in seconds.

K3 can access and analyze any data, thanks to its low-code environment.

K3 adroitly manages all three stages of ETL. Alooma offers little transformation functionality.

Platform Review

K3’s low-code interface and integration with all major databases, ecommerce platforms, trading, messaging, CRM, and ERP systems assures you can get the data you need quickly, transform it to the format your downstream components need, and combine and analyze it to generate the business insights that create sustainable competitive advantage.

As part of the Googleplex, Alooma purposefully walls off access to and communication with Snowflake, Redshift, and other leading data warehouse platforms. If you’re content with Big Query and are confident you will never need another data storage source, you might be able to make a marriage to Alooma work.

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

No

Database CDC Connections (see databases)

Yes

SaaS Connections (see connectors)

Yes

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

Yes

Features Comparison

Data Rules Engines

Yes

No

Low Code Data Route Configuration

Yes

No

Low Code ETL

Yes

No

Data Orchestration

Yes

No

Low Learning Curve

Yes

Audit Trail

Yes

Test/Prod accounts included

Yes

No

Capabilities and Functional Comparison

K3

Alooma

Deployment

K3 works as flawlessly through on-premise deployment as it does on the cloud, regardless of which file management system needs support. K3 is engineered and constantly upgraded to users in-house and SaaS cloud applications with little customization and no coding required. Data management, airtight connectors, and change data capture functionality provides scalable, flexible deployment for any application.

Alooma works well with Big Query, of course, as both swim in the pool of Alphabet soup. If you have drunk the Google Kool-Aid and possess the IT and development chops to develop the applications you need, Alooma may work. Most users we know, however, want the just-in-case ability to pull data from and store it in Redshift, Snowflake, and other common systems and to make their data accessible and usable by everyone in the organization, not just the pocket-protector crowd.

Data Load

K3 plays nicely with any data warehouse’s ground rules, adapting its features to present data in the configuration the platforms need, so it can be analyzed after K3 loads it. Unique k3 data load workflow precisely tunes the data schema to permit marketing, finance, operations, and other business intelligence systems to process it.

Alooma lets developers – if they are so inclined – to transform data only within its data pipeline rather than during and after the loading process. Most users require more tools for data warehouse management. Alooma’s extraction function works well, but its output management leaves plenty to be desired.

Data Rules

The K3 ETL data rules engine configures and loads the extracted data into target tables based on the robust search statements it generates from ETL rules. Users can develop workflows that govern any complex data environments in which data warehouses and lakes, integrators, downstream apps, and transactional processes exist.

Alooma’s Wild West forces designers to take the law into their own hands if they want to customize, test, and fix decision matrices.This makes validation and verification difficult as it can cloud the cause/effect optimal analysis hard to repeat.

K3 Wins Out With Extensive ETL and Integration Capabilities

It only makes sense to rely on an ETL platform that can integrate with all data sources, storage media, and operational applications. An extensive cupboard of K3 adapters and connectors deliver seamless interoperability every time. Alooma not only cannot match that performance, it doesn’t even try. Google assumes Big Query and Alooma offers everything a data-driven enterprise could ever need.

Upstream

Downstream