Yellowbrick
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Efficient scaling
Predictable Performance
Real-time Analytics

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Yellowbrick platform overview

Trusted by Leading Enterprises

SymphonyAI

5X faster cube build performance

Ushur

50% Reduction in DevOps Overhead

insightsoftware

4.5x faster queries and 300% lower TCO

Redshift's challenges

Amazon Redshift

Inconsistency & Bottlenecks

Unpredictable query performance, especially during peak workloads or high concurrency, impacts user experience and causes unnecessary delays in running your business.

High Scaling Costs

As data and user demands grow, spinning up new instances for every incremental set of users adds significant costs.

Load & Query Conflicts

Limited ability to load and query data simultaneously delays data availability and the speed at which your organization can make business-critical decisions.

Inconsistency & Bottlenecks

Unpredictable query performance, especially during peak workloads or high concurrency, impacts user experience and causes unnecessary delays in running your business.

High Scaling Costs

As data and user demands grow, spinning up new instances for every incremental set of users adds significant costs.

Load & Query Conflicts

Limited ability to load and query data simultaneously delays data availability and the speed at which your organization can make business-critical decisions.

Yellowbrick’s strengths

Yellowbrick

Consistency & Predictability

Advanced workload management ensures query performance remains consistent to ensure business goals are met in a mixed workload environment.

Cost-effective Scale

Scale on demand from one user with one database and gigabytes of data to thousands of concurrent users and databases with petabytes of data.

Load and Query Concurrently

Response times are not significantly impacted during bulk or streaming data loads, or during ELT processing.

Consistency & Predictability

Advanced workload management ensures query performance remains consistent to ensure business goals are met in a mixed workload environment.

Cost-effective Scale

Scale on demand from one user with one database and gigabytes of data to thousands of concurrent users and databases with petabytes of data.

Load and Query Concurrently

Response times are not significantly impacted during bulk or streaming data loads, or during ELT processing.

Learn How We Do it

What our customers say

insightsoftware Accelerates Analytics Services with Yellowbrick

"Yellowbrick outperformed Redshift in every metric—4.5x faster queries and 300% lower TCO."

Chris Barnes, VP of Engineering insightsoftware

Problem

Insightsoftware struggled with inconsistent query performance and high costs associated with Redshift, especially as they scaled their services and customer base.

Solution

Yellowbrick provided predictable and consistent query performance, significantly reduced TCO, enabled faster customer onboarding -- without the need for additional instances.

Results

Insightsoftware saw up to 4.5x faster queries and 300% lower TCO after migrating to Yellowbrick, enabling them to accelerate their data analytics services and scale efficiently.

Read the Story

Solution Briefs

Load and Query
Concurrently

Run analytics and data engineering together without conflict or complex orchestration.

Learn More about Load and Query Concurrently

Streaming
Ingestion

Stream, ETL, and query in the same database. Avoid multiple solutions.

Learn More about Streaming Ingestion

Keep Data
Local

Have absolute confidence in where data is located and processed.

Learn More about Keep Data Local

Elastic
Scale

Handle unlimited concurrency, multi-tenancy, AI complexity or massive volumes.

Learn More about Elastic Scale

Solution Briefs

Load and Query Concurrently

Run analytics and data engineering together without conflict or complex orchestration.

Learn More about Load and Query Concurrently

Streaming Ingestion

Stream, ETL, and query in the same database. Avoid multiple solutions.

Learn More about Streaming Ingestion

Keep Data Local

Have absolute confidence in where data is located and processed.

Learn More about Keep Data Local

Elastic Scale

Handle unlimited concurrency, multi-tenancy, AI complexity or massive volumes.

Learn More about Elastic Scale

Trusted Migration Process

Dedicated Yellowbrick Customer Success Team

  1. Step 1:
    Information Transfer & Assessment

    Objective: Gather necessary information and evaluate the customer’s environment.

    Action: Transfer details from the account team and validate workflow, architecture, and data sources (ETL, BI tools).

    Outcome: A complete understanding of the customer’s environment and migration components.

  2. Step 2:
    Build Migration Inventory

    Objective: Identify all components involved in the data migration process.

    Action: Compile an inventory of objects, views, schemas, stored procedures, tables, roles, UDFs, etc.

    Outcome: A comprehensive list of components required for migration.

  3. Step 3:
    Define Migration Path & Success Criteria

    Objective: Establish the migration approach and define success metrics.

    Action: Choose between Lift & Shift, object mapping, or code refactoring, and agree on functionality and performance targets.

    Outcome: A clear migration strategy and defined success criteria.

  4. Step 4:
    Plan, Execute & Validate Migration

    Objective: Plan, perform, and validate the migration, ensuring data integrity and performance standards.

    Action: Assign tasks, run Yellowbrick in parallel, and validate data consistency and performance under all conditions.

    Outcome: Migration is executed, with successful data validation and performance targets met.

Get a Migration Assessment

Transform data into action

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