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Are you drowning in Big Data? If
so, you’re not alone.
In today’s digital world,
everything that can be tracked, will be tracked. Data-driven technologies like
artificial intelligence and machine learning are promising major economic
benefits to those who can exploit them. This has left businesses and their data
science teams struggling to manage the massive datasets they’ve acquired.
In the data collection arms race,
everyone has ended up losing. The explosion of proprietary data has placed
intense resource requirements on data infrastructure and degraded business
intelligence (BI) tool performance.
This creates a conundrum. You need
fast insights, but for those insights to be useful they require lots of data to
back them up. This makes insight generation slower as your queries struggle to
sift through all that data.
Ultimately, it doesn’t matter how much data you have, that data is worthless if you can’t efficiently process it.
For organizations looking to improve their big data processing, extreme OLAP engines offer an easy path to success.
OLAP? Isn’t that technology kind of old?
It’s true OLAP technology has been around for a while, but it’s still just as relevant as ever. In fact, its maturity is an asset. It’s well understood, developed, and still outperforms newer technology approaches on the market today. If it can power the analytics of Global Fortune 100 companies, it’ll work for you, too.
When it comes to selecting the right solution, results can vary depending on IT infrastructure, preferred BI tools, and company size. Recently, Stratebi evaluated two popular proprietary and open source Big Data extreme OLAP technology products: Vertica and Apache Kylin (respectively). Both solutions performed well, but Stratebi’s results highlight the performance divides common with OLAP solutions on massive (petabyte) datasets.
Apache Kylin is the leading open source OLAP (online analytics processing) engine for Big Data. Capable of sub-second query latency on trillions of records, Kylin delivers a significant productivity boost to analytics operations in organizations of almost any size.
You can find more information here to learn the differences between Apache Kylin and Kyligence's Enterprise and big data analytics cloud products, and feature benchmarking Kylin and Kyligence against other solutions on the market.
Vertica is a highly-performant SQL-based OLAP data warehouse solution designed to handle high-speed ingestion and analytics for large-volume datasets. It promises to provide fast query performance in traditionally intensive scenarios, improve query performance over traditional database relational database systems, as well as provide high availability, and petabyte scalability on commodity enterprise servers.
So, what were Stratebi’s final
conclusions about Apache Kylin and Vertica. To get all the details, it’s
recommended you view the full report, but here are the highlights:
While Apache Kylin proved to be the
overall winner in performance, that doesn’t mean everything. Stratebi suggested
that other factors such as installation time and hardware requirements should
also be considered when conducting a thorough evaluation. With that in mind, Vertica
can be a reasonable choice for smaller operations with major resource
For smaller teams with budget
constraints but a bit more time, the open source nature of Kylin can be a
differentiator. If you’re looking for an OLAP processing engine that delivers
stunning results on enterprise-scale datasets, Kylin leads the way.
But your search doesn’t have to end there. Kylin and Vertica big data solutions are only two popular options. The early contributors to Kylin have also come together to offer Kyligence. Kyligence is powered by the same core technologies that makes Kylin so powerful, but with a suite of features that make it ideal for enterprise-scale Big Data work.
Features such as cell-level
security, multi-tenancy and BI vendor-proprietary connectors, make Kyligence a
perfect match for the complex IT needs all data-driven enterprises face today.
It also rewrites the storage to replace the Hbase that Apache Kylin uses which
simplifies maintenance and ensures more stable high query performance.
Companies that are able to
capitalize on their data will have a tremendous advantage over the competition.
This year, billions of dollars will be spent upgrading data warehouses and
software licenses for new BI tools. Unfortunately, many of those investments
will be squandered because of poor data management.
It doesn’t need to be this way. Don’t let Big Data turn into ‘Big Disappointment’. A Big Data OLAP engine may be the jumpstart your analytics operations need to extract more valuable insights.
...Before your competition does.
To get all the details of Stratebi’s benchmark research, you can view the full report here.
Also, if you hadn’t heard of Apache Kylin before this post, you can learn more about it here.
Get the facts about Apache Kylin and discover how its extreme OLAP technology matches up with Kyligence. Learn more on our Kylin vs. Kyligence comparison page. Want to get started on the path to faster augmented OLAP analytics right now? We recommend checking out this great video:
The driving force behind Meituan’s success is not simply a robust analytics system, but the OLAP engine that system is built upon - Apache Kylin.
Cloud Analytics News will share the important news on Apache Kylin, Kyligence Cloud, and related technologies. In this edition, we cover Apache Kylin 4.X beta, the launch of Kyligence Cloud 4, Pivot to Snowflake, and more.
UnionPay was able to consolidate the 1,200 Cognos cubes into 2 Kyligence cubes and a single ETL process. Besides extending the life of the analytics executed against this data, there was a massive improvement in operational efficiency.
A peek behind the curtain of the world's leading open source big data analytics project, Apache Kylin.
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