Analytics India Magazine, recently delved into why HPE Vertica 8 is the best fit for businesses to create an analytics-driven enterprise! [See the article, here.]
Without further ado, here are the top 7 reasons that make HPE Vertica 8 a good fit for every organization:
Reason #1: Vertica 8 has a definitive edge vs Hadoop-SQL combinations that are yet to achieve interactive performance.
HPE Vertica 8 not only provides SQL layer on top of Hadoop but it also supports fast data access to both ORC and Parquet. Vertica can connect and read data from any Hadoop instance. Everyday users rely on SQL query and Vertica 8 allows customers to access files in HDFS stored in ORC, Parquet format and achieve significant performance benefits compared to raw text files.
Reason #2: Expanded Cloud Integrations – Run Vertica in the loud that is relevant to the customer.
The idea behind expanded cloud integration was to “enable users to run Vertica in the cloud, that is relevant to them, without being locked into a particular cloud.” This gives the customer the choice which cloud to run in – with the expanded cloud support, it is about more deployment options, but helping customers manage it well in the cloud. The multi-cloud integration is in line with HPE’s strategy of helping customers accelerate their digital transformation. As part of the latest release, HPE Vertica 8 now supports Microsoft Azure Cloud and the new release also features expanded AWS support with access to S3.
Reason #3: Advanced compression capabilities deliver results at speed and scale.
As opposed to legacy technologies, HPE Vertica 8 has a full-featured analytics system that reduces big data analyticsquery to minutes and even seconds. The power of Vertica lies in parallelism and there are many emerging use cases where customers are finding more ways to use this architecture. High degree of concurrency and parallelism is at the heart of Vertica’s success and massively parallel processing can handle data at petabyte scale and speed.
Vertica gives a compression ratio of approximately 1:8 or maybe beyond and the benefits are twofold – a) data gets compressed; b) significantly reduces footprint for the infrastructure and reduces the cost. HPE Vertica 8 platform is 50x–1,000x faster than legacy data warehouse solutions and cranks out 10x–30x more data per server.
Reason #4: Expanded analytical database support for Kafka, Spark and Hadoop
Vertica 8’s extensive integrations to Apache Spark, HDFS and Kafka allows users to analyse the data where it resides. The open format means customers can analyse data as it is without transforming or moving it. Which means that instead of dealing with big batch loads of data every few hours, microbatch streaming has become one of the most popular ways of getting data into Vertica.
Vertica provides Kafka Connector to support real time stream data ingestion from different sources. It also provides Spark connector so Spark applications can read from Vertica database into Spark memory and can be processed along with machine learning algorithm.
Integration with Hadoop to support largest ingestion is a strong spot for Vertica. Vertica provides capability to read and write into Hadoop and can read different files formats from Hadoop like ORC, Parquet, Avro, Json. It also enables users to stores its own data file into Hadoop that makes Vertica flexible to integrate with any Hadoop distribution and act as a fast data processing layer along with Hadoop.
Reason #5: HPE Vertica 8 is the right choice for big analytics workloads.
The new-age analytics platform is specifically designed for big analytics workloads and packs a wide range of analytical functions to support faster decision making. It is suitable for OLAP applications as opposed to OLTP application since Vertica is very fast on data ingestion, querying and analysing patterns. Vertica is packed with inbuilt prediction modelling, sentiment and geospatial analytical capabilities that gives it an edge over other competitors. As part of the latest release, Vertica also has support for native machine learning algorithms.
One of the key advantages of HPE Vertica is that queries run faster — 50–1,000x faster than any data warehouse. Vertica’s in-database machine learning enables users to embrace Big Data and accelerate business outcomes. Under the new release, for in-database machine learning, the parallel machine learning algorithms have been brought inside Vertica, so that users can effectively analyse data and make predictions without exporting it out of Vertica.
Use cases:
- MTS India has made HPE Vertica a key part of their core telecom business and the proof lies in the number. The telecom player leverages the big data platform for driving accurate, targeted customer campaigns to understand how customers were responding to promotions – outcome resulted in doubling the conversion rate. The Big Data implementation at MTS India has significantly brought down the overheads in business operations.
- When ICICI Bank required information on whether they should deploy more ATMs in a particular region or understand the kind of transactions customers were having, they used HPE Vertica Analytics Platform built-in geospatial analysis function to get insights. One of India’s leading banks, ICICI has approximately 4000 ATMs across the country and depending on the geography and population, the bank wanted to ascertain whether they should be set up more ATMs.
Reason #6: HPE Vertica 8 shines on scalability, speed & performance.
It’s been more than a decade since Michael Stonebraker, database pioneer whose groundbreaking idea of an architecture that stores data in columns rather than rows, paved the way for HPE Vertica. Now, its mature columnar storage makes ‘hot data’ available faster than a traditional RDBMS solution and that makes it the right fit for large enterprises demanding high performance and ease of scalability.
Vertica works on Massive Parallel Processing (MPP) concept that provides high performance and scalable architecture on commodity hardware. It means, nodes can be added on the fly and Vertica does automatic data balance within all nodes i.e. within cluster. The performance automatically increases as you scale out the cluster size. It helps to reduce the cost and further reduces TCO for running the system.
Features like Parallel Load and Apportioned Load options makes data load option faster in Vertica 8. Case in point, a single large file or other single source is divided into segments, that are assigned to several nodes to be loaded in parallel. Vertica also provide option to define target wise loading in advance which makes system understand the loading strategy in advance, thereby yields to faster data loads in the system. The speed and scale at which data ingestion happens in Vertica 8 is phenomenal. Customer sessions reveal the scale at which Vertica operates is mind boggling with users loading 3000 unique tables an hour, each table featuring up to 1500 columns, underscores the volume of data ingestion.
Reason #7: HPE Vertica’s pricing model makes it a good fit for big & small players.
The right big data analytics platform is one which fits the needs of large organizations and SMBs. HPE Vertica’s pricing model scores big from a cost perspective. Haresh Krishna Kumar Nese, HPE Country Manager – India & SAARCexplains why the HPE Vertica is the best choice for organizations, big and small alike. “SMBs have high requirement for solutions and they need analytics to improve operations and generate new revenue streams. Hence, we have reduced the entry barrier by offering a subscription based model so that smaller players can also adopt HPE Vertica platform and make better business decisions,” Nese stated. The subscription model strategy seems to have paid off well, since most customers worry about a “Vendor lock-in.”
- Licensing Model: Under the perpetual licensing model, customer can invest in a license and get the support features as well.
- Subscription Model: The subscription based model has significantly reduced the entry barrier for SMBs that can adopt advanced analytics database platform by paying a less amount upfront. By removing in a lock-in period, the entry barrier has been significantly minimized.
