Services (built)

don’t go in the lake without us

Tackling Data Lake Complications

Big data lakes are becoming key components to next-generation enterprise data architectures, and are handling a wide variety of mission-critical enterprise workloads. Because of this, the requirement to understand the data in the lake, its provenance, its quality, and the processes by which it was created or transformed has become paramount.

Hadoop data lakes present the following challenges for today’s enterprise:

Building Hadoop data lake

  • Rate of Change:Keeping up with constantly evolving Hadoop ecosystem
  • Skills Gap:Lack of expertise in both development and architecture
  • Complexity:Numerous components to integrate: Hardware, software, applications

Managing Hadoop data lake

  • Ingestion:Difficulty getting data into data lake effectively
  • Lack of Visibility:Lack of data visibility and transparency
  • Privacy and compliance:Addressing data privacy and compliance issues

Deriving value from Hadoop data lake

  • Quality issues:Need for improved data quality control
  • Reliance on IT:Business users must rely on IT to prepare data for analysis
  • Reusability:Lack of automation means constantly re-creating the wheel

Zaloni built its Bedrock Data Lake Management Platform to solve these challenges. Battle-tested by F100 customers, Bedrock enables visibility, governance and reliability of data in the data lake.

Capabilities of the Bedrock Data Lake Management Platform


Unified Data Management: Integrated solution provides a single interface from which to leverage best-of-breed Hadoop ecosystem components

Simplified Onboarding of New Data: Managed so that IT knows where data comes from and where it lands

Data Reliability: Confidence that your analytics are always running on the right data, with the right quality

Data Visibility: Metadata management capabilities allow you to keep track of what data is in Hadoop, its source, its format and its lineage

Data Security: Ensures access control and provides data masking/tokenization for privacy initiatives

Reduced Coding: Drag and drop GUI abstracts coding for various Hadoop components, enabling non-experts to automate ingestion, create workflows and build queries