outgrowing redshift?
we can help.
Efficient scaling
Predictable Performance
Real-time Analytics
Trusted by Leading Enterprises
5X faster cube build performance
50% Reduction in DevOps Overhead
4.5x faster queries and 300% lower TCO
Trusted by Leading
Enterprises
5X faster cube build performance
Redshift's challenges
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.
redshift’s
challenges
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
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.
yellowbrick’s
strengths
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.
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
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.
solution briefs
Load and Query
Concurrently
Run analytics and data engineering together without conflict or complex orchestration.
Streaming
Ingestion
Stream, ETL, and query in the same database. Avoid multiple solutions.
Elastic
Scale
Handle unlimited concurrency, multi-tenancy, AI complexity or massive volumes.
solution briefs
Load & Query Concurrently
Run analytics and data engineering together without conflict or complex orchestration.
Streaming Ingestion
Stream, ETL, and query in the same database. Avoid multiple solutions.
Data Residency & Security
Have absolute confidence in where data is located and processed.
Elastic Scale
Handle unlimited concurrency, multi-tenancy, AI complexity or massive volumes.
trusted migration process
DEDICATED YELLOWBRICK CUSTOMER SUCCESS TEAM
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.
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.
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.
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.
