SOLUTIONS for DATA LAKES AND LAKEHOUSES
Diskover reveals the depths of your data lakes and lakehouses.
Diskover empowers organizations to harness the full potential of their data lakes and lakehouses by providing comprehensive visibility, metadata enrichment, and streamlined data management across diverse data sources.
Partners we’re diving in with.
DISKOVER BRINGS STRUCUTRE AND PRECISION
There are many data voids. We can fill them.
DATA VOIDS
THE DISKOVER EDGE
No filesystem and metadata in data lakehouse.
Adds filesystem structure and metadata to data lakehouse.
Metadata context from APIs and databases.
Adds additional metadata collected from databases and software platforms/APIs about indexed files to Lakehouses.
Data identification and data quality.
Crowd-sourced data identification improves the quality of the data in the data lakehouse to train models.
Increasing infrastructure costs due to large data transfer.
Keep infrastructure costs lower by only delivering curated data.
Irrelevant data being moved into AI data platform.
Continually keep AI platform data lakehouse updated with the latest data sets to provide the most relevant predictions.
Extended data processing times.
Reduce processing time by harvesting file data before it enters your lakehouse.
Metadata collection from various data sources.
Consolidate unstructured metadata from hybrid environments into one Lakehouse view.
DISKOVER OPTIMIZES WHAT FLOWS INTO YOUR LAKE
Take the murkiness out of your swampy data.
DATA LAKES
Dive into clarity.
Diskover helps transform a data lake into a well-organized, easily accessible resource that delivers valuable insights. Whether for streamlining day-to-day operations, enabling advanced analytics, or supporting strategic decision-making, Diskover unlocks the full potential of its data lake and drives meaningful outcomes.
Diskover offers comprehensive visibility into the contents of your data lake, indexing vast amounts of unstructured and structured data to make it searchable and easily accessible. This visibility helps users quickly locate relevant data without sifting through unorganized repositories, reducing the risk of a “data swamp.”
By extracting and organizing rich metadata, Diskover helps users understand the context of their data, enabling smarter insights and more informed decision-making. This metadata enrichment is crucial for effective data governance and compliance.
Diskover enables organizations to classify and tag data within the lake, organizing it by type, age, relevance, or custom criteria. This helps in segmenting data that’s frequently accessed from infrequently accessed data, supporting storage optimization and cost savings.
Diskover allows for automated actions, such as archiving, deletion, or migration, based on specific data criteria. This ensures that data is managed effectively over time, reducing storage costs and enhancing data lake performance.
For organizations looking to leverage AI and machine learning, Diskover can identify relevant data sets and prepare them for training and analysis, streamlining the workflow for data scientists and reducing the time spent on data wrangling.
Improved collaboration and access control.
Diskover’s search and access controls make it easier for teams to collaborate on data within the lake, ensuring that the right people have access to the right data, while maintaining data security.
DATA LAKEHOUSES
Lakehouse, meet your metadata engine.
In a data lakehouse environment, Diskover amplifies the efficiency and accessibility of data, ensuring both structured and unstructured data can be easily found, managed, and prepared for direct analytics. This holistic approach helps organizations achieve the full potential of their data lakehouse, maximizing insights and minimizing complexity.
In a data lakehouse, both raw and structured data coexist. Diskover can index and make searchable all types of data, providing comprehensive visibility across unstructured data, semi-structured logs, and structured tables. This unified view is ideal for environments where data variety is high, as it allows quick, seamless access across all data types.
Diskover’s classification and tagging capabilities allow for easy segmentation and organization of data, helping prioritize and curate data for specific analytical needs. In a data lakehouse, this is especially valuable for preparing structured data sets for direct querying or machine learning pipelines, improving data quality and reducing data preparation time.
Diskover extracts rich metadata across both structured and unstructured data, offering detailed context and insights. This metadata enrichment supports a data lakehouse’s need for both high-level overviews and granular control, making it easier to manage and optimize data for different analytical queries and use cases.
Diskover enables automated data management actions—such as tiering, archiving, or migrating data based on usage or relevance—that help maintain the efficiency of a data lakehouse. In addition, Diskover’s customizable access controls ensure that only authorized users can access sensitive data, aligning with the data governance needs of a lakehouse.
A data lakehouse often supports AI, machine learning, and business intelligence tools directly. Diskover helps identify and prepare relevant data sets, ensuring that data scientists and analysts have easy access to high-quality, curated data for faster AI and BI workflows without moving data between platforms.
Preventing data swamping and redundancy.
Diskover’s powerful indexing helps prevent common data lake challenges, like “data swamping,” by making it easy to identify redundant or obsolete data. This capability is particularly valuable in a lakehouse setup, where maintaining clean, optimized data is crucial for efficient query performance.
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