ChaosSearch Named in 2022 Gartner® Market Guide for Analytics Query Accelerators
Last week, Gartner released their 2022 Gartner Market Guide for Analytics Query Accelerators (AQAs), which names ChaosSearch as a Representative Vendor.
In this week’s blog, we share perspectives from both Gartner and ChaosSearch on the emerging AQA category that focuses on accelerating data lakes’ time to value. You’ll learn what AQAs are, their role in the enterprise tech stack, and how ChaosSearch differentiates itself from others in the segment.
What is an Analytics Query Accelerator?
An analytics query accelerator (AQA) is software that plugs into a data store (e.g. data lake, data warehouse, or data lakehouse), and makes the data more accessible and performant for BI and exploratory use cases. AQAs deliver analytics features on top of data storage, including SQL or SQL-like query support, BI dashboards, interactive querying, and data modeling.
According to Gartner, “Analytic query accelerators provide optimization for diverse data stores associated with data lake, data warehouse, and lakehouse architectures.”
Gartner also says that AQAs are commonly deployed in conjunction with data lakes. That’s true for the ChaosSearch Cloud Data Platform, which attaches directly to clients’ GCP or AWS cloud object storage to activate the lake for analytics.
AQAs are an Emerging Data Analytics Solution
The inaugural edition of the Market Guide for Analytics Query Accelerators was published in 2020, making AQAs one of the newest up-and-coming product categories as identified by Gartner.
Gartner says, “Interest in analytics query accelerators is increasing as data analytics leaders continue to struggle with getting value from their data lake initiatives… The market is a logical extension of the SQL interfaces to Hadoop and the SQL interfaces to cloud object stores — both of which are featured in the Hype Cycle for Data Management, 2021.”
(Note: ChaosSearch was also named in the Gartner Hype Cycle™ for Data Management 2021 report, as a Sample Vendor in the Data Lake and Data Lakehouse categories.)
Why are AQAs Important in the Enterprise Tech Stack?
“Data and analytics leaders continue to struggle with getting value from data lake initiatives that have grown to be unwieldy or that cannot deliver adequate performance as they have evolved,” Gartner says.
We at ChaosSearch have always believed in a data lake philosophy that values easy data ingestion with a schema on read approach, loosely coupled storage/compute to maximize efficiencies, and multi-API access to support flexible use cases and drive innovation.
For businesses struggling to get value from their data lakes, the problem usually isn’t getting the data in — it’s getting value out, while minimizing cost, toil, and complexity.
Gartner states that data lake infrastructure is “generally unable to optimize for the demands of production delivery to the degree that the data warehouse can when built on a relational database.”
Businesses are capturing and storing data in their data lakes at scale, but traditional data lakes weren’t built for high performance analytics. Instead, the data must be cleaned, normalized, transformed, and moved to downstream analytics tools before it can be analyzed.
This makes it costly, complex, and time-consuming to extract insights — especially as businesses experience unprecedented data growth and capture huge volumes of data. Over time, the data lake can decay into a data swamp: a disorganized mess of unmanaged data that provides little value.
This is exactly the problem that ChaosSearch was built to solve: breaking down silos and making data more accessible (discoverable, visible, etc.) so enterprises can Know Better® and ultimately activating the data lake for analytics at scale. Our cloud data platform attaches directly to clients’ public cloud storage, automatically discovering and indexing data in our proprietary Chaos Index® format, and providing SQL querying and full-text search on JSON, CSV, text/log, and Apache Parquet files.
Gartner recognizes the role that AQAs can play in helping enterprises extract value from their data, saying “Data and analytics leaders should use [AQAs] to accelerate time to value of their data lake initiatives.”
What Makes ChaosSearch Different?
In a product category where features like SQL query support and BI dashboards are table stakes, the most successful companies will differentiate themselves from the pack with unique innovations that help their customers achieve more.
Here’s what differentiates ChaosSearch:
“With a strong historic focus on log analytics, its supported sources are text/log files, Apache Parquet, JSON and CSV. ChaosSearch persists data inside a client’s VPC. Its optimizations include those for data layout optimization, distributed shared memory, materialized views and the use of storage indexes. ChaosSearch provides visualizations via its own interface, Kibana, and Elasticsearch, and provides open APIs for expanded search and SQL access to BI and analytics tools. Role-based access control is provided, as well as integration with common single sign-on (SSO) providers. GDPR-compliant log analytics has been a key driver. The firm refers to client use of analytics tools like Kibana and the Elasticsearch API with ChaosSearch. ChaosSearch is extending its portfolio to support data lakes more broadly with availability to run on the Azure cloud platform and deliver direct API access for machine learning libraries. The company raised $40 million in Series B funding in December 2020.”
ChaosSearch lets you index and query data directly in your public cloud storage, so there’s no data movement, no costly ETL process, and you retain full control of your data.
Our patented Chaos Index® fully indexes the data in your cloud with 10-20x compression, dramatically reducing the cost to store, index, search, and query from your data lake.
From our perspective, data lake initiatives often fail to deliver value because they’re using outdated technology and methods — not because the data lake philosophy is wrong.
Now, with solutions like ChaosSearch that can plug into the cost-effective, scalable, and durable public cloud data lake, enterprises can activate the data lake for analytics and deliver faster time to value from these investments.
Read the Full Report
Check out the full Gartner report to learn more about how:
- Gartner defines an analytics query accelerator
- Data and analytics leaders can improve the time to value of their data lake initiatives with technologies that provide SQL query support on a broad range of data sources
- Organizations should consider which analytic workloads can run in the data lake vs. moving them out to the data warehouse
Check out the Report: 2022 Cloud Data & Analytics Survey Report
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