Enterprise Data Lake

Effortlessly consolidate data into your Data Lake

More organizations are adopting data lakes as part of their architecture for their low cost and efficiency in storing large volumes of data.

The idea is simple: Instead of storing data in a purpose-built data store, you move it into a data lake in its original format.  This eliminates the upfront costs of data ingestion and transformation. Once data is in the lake, it’s available to everyone in the organization for analysis.

But while a data lake is an efficient solution, it does not come without challenges:

  • Initial onboarding of data from multiple sources
  • Continual updates in real-time
  • Moving high volumes of data to the data lake while mitigating chatter and latency

Enterprise Data Lake

Get the right features for consolidating and moving data into your Enterprise Data Lake

We enable you to easily move your data into your lake and update your data lake in real-time. HVR’s solution includes:

  • Initial data load from multiple sources to the data lake
  • Log-based change data capture for real-time updates
  • Compare and repair feature to ensure data accuracy

Benefits of using HVR to feed your data lake include:

  • Efficiently load and update data
  • Move high volumes of data for real-time analysis
  • Accelerate data movement with minimal impact on systems
  • Scale the solution for multiple projects and systems

Data Lake consolidation

Commonly Asked Question: Can the Data Lake Replace My Data Warehouse?

From what we have observed in the marketplace and talking to customers about their logical reference architecture, there is still a need for data warehousing. For all of Hadoop’s hype, it is still in its infancy to generate the kind of performance for doing complex queries and mixed workloads, lacking the kind of features that made data warehousing a must. e.g. optimal indexing strategies, efficiently performing complex table joins with a range of terabytes of data, and an optimizer for determining the best path for queries.

Data Warehousevs.Data Lake
Rigid, structured and needs to be processedData Structured, semi-structured and unstructured in its raw format
Schema on writeData Analysis StrategySchema on read
Enterprise, strategic reporting Reporting Discovery, operational reporting
Expensive as data volumes growStorageUsed commodity hardware which is typically cheaper

But when you combine all these technologies together you eliminate all the disadvantages and reap all the benefits. Granted that not all companies will require all these technologies in a single moment, what technology you deploy will be based on your end-user requirements and data you are pulling in. But what is fundamental in these architectures is the combination of a data lake and data warehouse working in a unified manner. Read more about data lakes vs data warehouses in our blog post.

Enterprise Data Lake+Data Warehouse

Enterprise Data Lake Architecture

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