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Integrations / Retail / Ocado

Move Ocado Data into a Data Warehouse in Minutes

Ocado is an online supermarket that operates its own propriety online market software and collects data on customer transactions for analysis. Panoply’s simple step by step process makes it possible to integrate Ocado data into your own data warehouse in a few seconds, without requiring a complex data infrastructure or ETL specialists.

Panoply scales to the size of your data inventory and can combine Ocado data with all other datasets and data sources. Your analysts can begin generating reports and dashboards from directly within the Panoply platform and start providing your company with actionable strategies in minimal time. Create a better customer experience and analyze Ocado sales from within your own Smart Data Warehouse, with Panoply.

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Panoply makes it easy to analyze Ocado data

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Why Panoply?

Use-Case Optimization

Analyzes queries and data – identifying the best configuration for each use case, adjusting it over time, and building indexes, sortkeys, diskeys, data types, vacuuming, and partitioning.

Query Optimization

Identifies queries that do not follow best practices – such as those that include nested loops or implicit casting – and rewrites them to an equivalent query requiring a fraction of the runtime or resources.

Server Optimization

Optimizes server configurations over time based on query patterns and by learning which server setup works best. The platform switches server types seamlessly and measures the resulting performance.

Updates, Upserts, and Deletions

Supports standard SQL update and upset operations out of the box – without worrying about vacuuming or rebuilding- unlike many analytical databases.

Semi-Structured Data Parsing

Supports semi-structured text values like nested JSON, user-agent strings, some standard log formats, CSV, and serialized Ruby objects – parsing these objects and normalizing them into a relational database design.

Nested Structures

Handles nested structures automatically, flattening them into several tables with a one-to- many relationship. The result is a ready-to- use relational database design for all current and future datasets.

Columnar Storage

Provides seamless data storage and management in a multitiered, columnar storage based on Amazon Redshift, Elasticsearch, and Hadoop or SS3.

Data Tracking and Alerts

Simplifies how you keep track of vast amounts of data – by identifying patterns, providing notification of anomalies, and generating alerts when the results of arbitrary SQL queries exceed predefined thresholds.

Backup and Recovery

Automatically backs up changes to data to a redundant S3 storage, optionally saved in two different availability zones across continents – enabling full recovery to any point in time.