INFRASTRUCTURE Solution

Optimize Your Lakehouse Tables

Accelerate performance & reduce costs with data lakehouse table optimizations for Apache Hudi, Apache Iceberg, and Delta Lake

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Advanced Tools to Accelerate your Data Lakehouse and Reduce Costs

Accelerate Time to Analytics

  • With minimal configuration updates to your existing data pipelines, uncover inefficiencies and deliver up to 10x faster analytics.
  • Enhance ingestion and query performance of all data lakehouses with table cleaning, clustering, compaction, file-sizing, and more.

Keep Costs Down with Automation

  • Eliminate burdensome manual debugging and maintenance for table cleaning, clustering, compaction, file-sizing, and more.
  • Simply configure a few services and Onehouse will auto-tune your data lakehouse — and keep it tuned.

Observability & Monitoring

  • Track key metrics, dashboards, and pre-built visual insights about your data lakehouse to spot patterns and optimize storage and performance.
  • Understand the state of your pipelines and tables. Track key metrics with pre-built dashboards and receive customized weekly review emails so you can optimize your tables with insights into partitions and data skew.

Eliminate Manual Tuning and Maintenance

Data lakehouse pipelines require significant manual tuning. Onehouse enables a hands-free approach to optimizing your lakehouse tables.

Key Features for Accelerated Data Ingestion

Intelligent incremental clustering

  • By making reads more efficient with incremental clustering, accelerate read performance and reduce overall costs.
  • Configurations include keys, layout strategy, and frequency.

Async compaction

  • Improve write performance with async compaction to merge incoming data into the table. If you are using Apache Hudi, this is particularly useful for Merge-on-Read pipelines.
  • Configurations include frequency of runs and bytes per compaction.

Advanced cleaning

  • Easily remove old data already committed to a table and beyond your time-travel retention policies to keep storage costs down.
  • Configurations include frequency and time travel retention policies.

Key Features for Accelerated Data Ingestion

Intelligent incremental clustering

  • By making reads more efficient with incremental clustering, accelerate read performance and reduce overall costs.
  • Configurations include keys, layout strategy, and frequency.

Async compaction

  • Improve write performance with async compaction to merge incoming data into the table. If you are using Apache Hudi, this is particularly useful for Merge-on-Read pipelines.
  • Configurations include frequency of runs and bytes per compaction.

Advanced cleaning

  • Easily remove old data already committed to a table and beyond your time-travel retention policies to keep storage costs down.
  • Configurations include frequency and time travel retention policies.

Uber’s Universal Data Lakehouse Success

50%

Reduction in pipeline run times

80%

Reduction in ETL time for critical tables

97%

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Firstname Lastname
Title, Company

Uber’s journey

Uber’s Universal Data Lakehouse Success

50%

Reduction in pipeline run times

80%

Reduction in ETL time for critical tables

97%

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation.

Firstname Lastname
Title, Company

Uber’s journey

Learn How to Optimize Your Lakehouse Tables

Quickstart guide

Lakehouse Table Optimizer
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webinar

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Quickstart guide

Lakehouse Observer
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Quickstart guide

Lakehouse Table Optimizer
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