How a Data Lake Query Acceleration Platform can Reduce Latency for Data Lakes and Expand Adoption

The exponential growth of data generated and managed by companies around the world is driving organizations to look for better ways to keep the data safe and organized. Data lakes offer a way to store unstructured data. But making the most of querying data lakes can be a challenge for data analysts. Here’s how a query acceleration platform can ensure fast and effective queries for data lakes.

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Why and who uses a data lake? 

A data lake is a repository that can hold a large amount of raw data until the company needs to use it. It can store unstructured and structured data as-is, without having to structure the data. It is useful for companies in regulated industries like healthcare or finance, which need to store huge datasets of private or sensitive user data. 

How a Data Lake Query Acceleration Platform can Reduce Latency for Data Lakes and Expand AdoptionImage credit: AWS

Data lakes also enable you to store relational and non-relational data like collected data from mobile apps, IoT devices, or social media. You can understand the data in the lake through crawling, indexing, and querying. 

Why do you need a data lake? According to a survey, organizations that implemented a data lake saw 9% more growth. Data lakes allow the efficient storage of data. It also enables understanding data for quick queries and advanced data analytics. 

Benefits of a data lake

Some of the business benefits of a data lake include: 

  • Stores the data for further exploration: a data lake preserves raw data in case a data scientist or auditor needs to access it later. It also gives an environment for data analysis without the need to model and load the data. 
  • Is easily scalable: Data lakes offer scalability at a much cheaper price than other options by using scalability tools like Hadoop which makes it easy to accommodate a growing amount of data.
  • Breaks down silos: having data in different storages makes it difficult to handle and understand. Data lakes solve this problem by storing all the data in one central location but all authorized users have access to the data lake for analytics. 

Data Lakes challenges

However large the benefits a data lake can offer for businesses, sometimes data lake architecture stays as a storage layer. The challenge remains how to leverage big data so it can solve business problems by agile and flexible analytics. Many companies still underutilize data lakes by only running ad hoc queries and failing to make the most of their data. 

  • Moving data for queries: often, when data consumers need to use the data in the lake the data needs to be transferred for custom optimizations and analysis. This is a time-consuming and costly process. 
  • Budgeting for ETL and storage: querying data in a data lake often requires moving the data by extract, transform and load (ETL). This process can be expensive and get over the budget.  
  • Governance: data lakes are typically very regulated as they often contain personal or sensitive data. Regulations like GDPR and CCPA require a company to delete a customer’s data if they request it. This process is computer-intensive, as it requires identifying, ingesting, filtering, and deleting the original data. All this without disrupting other queries on the table.  
  • Performance: data lake query engines, such as PresoDB and Trino, are often based on brute force technologies that are compute-intensive and may not comply with performance and concurrency requirements.

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What is query acceleration?

Query acceleration is a process that enables analytics frameworks to optimize data processing by retrieving only the data required to perform an operation. Query acceleration accepts filtering predicates and column projections at the time the data is read from disk, reducing latency. 

Query acceleration advantages 

  • Optimizes performance by reducing the amount of data transferred and processed. 
  • Applications can save costs by transferring computer load to query acceleration.
  • It is designed for data processing applications that perform large-scale data transformations. 

3 reasons a data lake query acceleration platform solves query challenges

Just adding a query engine on your data lake won’t optimize the performance and costs. Typically, data lake-based query engines have basic controls. A data lake query acceleration platform gives you visibility, advanced controls, and automation to make the most of the data. 

  • Leverage data virtualization to reduce costs.

Keeping spending under control is critical for a data lake. The data virtualization capabilities of a query acceleration platform enable data scientists to prioritize and allocate resources. It also allows optimizing new workloads based on pre-set priorities. 

  • Complete visibility of workload performances

With a data lake query acceleration platform, data teams gain complete visibility into the workload performance. The automation features enable the platform to monitor and optimize workloads to meet performance and budget requirements. A query acceleration platform identifies bottlenecks and issues, adjusting the storage and performance. 

  • A data lake specific platform optimize speed and lowers latency

Simple query engines often only let you control how fast the engine scans data. They also can show a query status but the capabilities are limited. Speeding up the number of completed queries often requires running fewer queries at a time. To solve that, you often end up paying for more disks to be able to scan and speed up the queries. A query platform gives visibility and automates prioritizing queries. 

Ultimately, a query acceleration platform gives you automated query optimizations that reduce latency, gives you visibility to prioritize and scale-out without going over budget. 

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