Information Faster Blog

New Release of “DbVisualizer Free for Vertica” Now Available via the HP Haven Marketplace

Version of DbVisualizer Free for Vertica is now available!


Expanding on features normally reserved for the Pro version, DbVis has added the “Connection Keep Alive” feature to DbVisualizer Free for Vertica. The Connection Keep Alive feature issues a simple database “ping” to the database server at a specified interval preventing time-outs and lost connections as a result of being idle in DbVisualizer.


In addition to supporting Vertica Flex Tables, UDFs, and projections, other new features available in version include “Editor Templates” that can be used to easily insert text that you often use in SQL statements, and ”Master Password” that improve the encryption of all your saved passwords.


Version also includes some bug fixes.


To experience these and other new features of DbVisualizer Free for Vertica, simply visit the HP Haven Marketplace to download the latest version.

Want to build a Big Data Analytics proof of concept in 15 minutes?

Have you seen the recently launched HP Vertica - Hortonworks Sandbox?  Available now, you can download this combined Sandbox for free from the HP Haven Marketplace. 


With this pre-configured Sandbox you can see HP to Hadoop integration features including the:


- HP Vertica HDFS connector

- HP Vertica HiveMetastore Integration

- HP Vertica Storage Locations on HDFS

- HDP Integration to HP Vertica data


This Sandbox provides a personal, portable HP Vertica – Hadoop environment that comes with a dozen interactive Hadoop tutorials.  It includes many developments from the latest HDP distribution, packaged up in a virtual environment that you can get up and running in 15 minutes. The added bonus in this sandbox is a functional HP Vertica server.  HP Vertica and the Hortonworks Data Platform were combined in the same virtual linux machine, complete with a web demo front end which demonstrates HP Vertica SQL integration with HDFS and HiveMetastore as well as showing how to setup and use HDFS as a HP Vertica storage location.  


Now you can learn Hadoop, HP Vertica, Build a Proof of Concept and Test new Functionality, all in one place!

HP Takes Big Data to the Cloud

Last week, we announced HP Haven OnDemand at HP Discover 2014 in Barcelona, Spain. This exciting new set of cloud offerings includes HP Vertica OnDemand and HP IDOL OnDemand to manage structured and unstructured data including business data, machine/IoT data and human generated content.

Happy Holidays to the Big Data Ecosystem!

Happy Holidays to our Ecosystem partners - and customers - from the HP Big Data Business Development team. We're looking forward to a Big (Data) 2015!

Workload Management Metrics – A Golden Triangle

Modern databases are often required to process many different kinds of workloads, ranging from short/tactical queries, to medium complexity ad-hoc queries, to long-running batch ETL jobs to extremely complex data mining jobs (See my previous blog on workload classification for more information.) DBAs must ensure that all concurrent workload, along with their respective Service Level Agreements (SLAs), can co-exist well with each other while maximizing a system’s overall performance.


So what is concurrency? Why should a customer care about concurrency?


Concurrency is a term used to describe having multiple jobs running in an overlapping time interval in a system. It doesn't necessarily mean that they are or ever will be running at the same instant. Concurrency is synonymous to multi-tasking and it is fundamentally different from parallelism, which is a common point of confusion. Parallelism represents a state in which two or more jobs are running at the exact same instant. The simplest example might be a single CPU computer. On such a computer, you can, in theory, run multiple jobs by context-switching between them. This gives the user the illusion of virtual parallelism or that multiple jobs are running on the single CPU at the same time. However if you take a snapshot at any given instant , you’ll find there is one and only one job running. In contrast, actual parallel processing is enabled by multiple working units (e.g. multiple cpu/cores in a modern database server such as the HP DL380p). Because Vertica is an MPP columnar database and an inherent multi-threaded application, it can take advantage of this multiple-CPU/core server architecture to process queries in both a concurrent and a parallel manner.


Most customers do not usually care about concurrency directly. Rather, they have a specific requirement to execute a certain workload in a database governed by a set of throughput and response time (latency) objectives. Throughput (TP) is defined as the number of queries/jobs that a database can perform in a unit of time and is the most commonly used metric to measure a database’s performance. Response time (or latency) is the sum of queuing time and runtime and as such it depends on both concurrency (as a proxy for overall system load) and query performance (= inverse of runtime).


For a given workload, the three metrics: throughput (TP), concurrency, and performance are related to each other by the simple equation:


Throughput = Concurrency * Performance Knowing any two of these three metrics, you can derive the third. This relationship can be visually illustrated by the following Workload Management Metrics Triangle: workload_golden_triangle


Concurrency is often NOT a direct customer requirement because it depends on query performance and throughput SLA. Customer requirements are usually in the form of something like this: “We need to process 10K queries in one hour with an average response time of 1 min or less.” So throughput (TP) is often the metric that customer is interested in and concurrency is a “derived” metric.


Let’s consider a hypothetical customer POC requirement of processing twelve hundred queries in one minute and assume that there are two competing systems, X and Y.


On System X, executing such a workload would require a currency level of 40 with an average query runtime of 2s.


On System Y, assuming average query response is 100ms, executing the same workload, requires a concurrency level of only 2 (because 20/s=2*1/100ms).


What does this mean for the customer? Clearly System Y with its superior query processing capability needs far less concurrency to satisfy the SLA than System X and hence it is a better platform (from a purely technical perspective).


To summarize, for a given throughput (TP) SLA, the better the query/job performance, the less concurrency it needs. Less concurrency generally means less or more efficient resource usage and better overall system performance (since there will be more spare system resources to process other workloads). The goal of any workload performance tuning exercise should never be about increasing concurrency. Instead it should focus on minimizing a query’s resource usage, improving its performance and applying the lowest possible concurrency level to satisfy a customer’s throughput (TP) and response time (latency) requirement.


Po Hong is a senior pre-sales engineer in HP Vertica’s Corporate Systems Engineering (CSE) group with a broad range of experience in various relational databases such as Vertica, Neoview, Teradata and Oracle.

Introducing Trafodion



On Tuesday, June 10, 2014 at HP Discover, Hewlett-Packard announced that the Trafodion project ( is now available as open source. Trafodion is an enterprise-class SQL-on-HBase solution targeting big data transactional or operational workloads. There have been many SQL engines or SQL subsets developed for the Apache Hadoop space, but most of those engines are focused on analytics. Trafodion is meeting a different need: The need for transactional and operational management of data. Using Trafodion with Apache HBase, it is now possible to support OLTP applications on Hadoop. As one of the architects of Trafodion, I’d like to give you a high-level overview of Trafodion’s features.


At a glance, Trafodion is a transactional ANSI SQL engine on top of Apache HBase. It has performance improvements for OLTP workloads. It also supports large data sets through parallelism. It offers JDBC and ODBC connectivity for Linux and Windows clients. We’ll go through some of these features in more detail.


 Transactions: Trafodion features ACID distributed transactions. This is a key feature for supporting OLTP. A transaction might involve multiple rows, multiple tables and/or multiple statements. Trafodion uses the Multi-Version Concurrency Control model. Instead of locking, Trafodion checks for conflicts between transactions at commit time. This is a good model when transaction conflicts are few. 


ANSI SQL: Trafodion supports the core feature set of ANSI SQL-99, with many extensions. Some noteworthy extensions include support for secondary indexes and cost-based optimization. You can create Trafodion tables within HBase. These tables are salted across regions on a set of columns you specify. You can also access native HBase tables directly. There is syntax for accessing individual HBase values. You can access multiple versions of values in one SQL statement.


Performance/parallelism: The Trafodion engine supports many optimizations, both compile-time and run-time. For OLTP, there are fast paths for single-row queries. For large data sets, the optimizer can choose a parallel plan. Trafodion’s optimizer searches a space of possible plans for one that is low cost. Determining cost depends in part on statistics about table data gathered with the UPDATE STATISTICS utility. The Trafodion optimizer also caches query plans, so frequently recurring queries need not be repeatedly optimized. Trafodion leverages the natural parallelism inherent in the multiple region servers in the HBase architecture. But it can go beyond that. Imagine, for example, that you are joining two tables, and the join is on a non-key column. Trafodion can generate a query plan using multiple execution servers, each of which will receive a dynamically generated partition of each table. The data are partitioned on a hash of the join column. Join methods supported include hash, nested loop and merge joins. Hash joins do not require all data to be in memory; the algorithm can spill intermediate results to disk files.


JDBC/ODBC: JDBC and ODBC drivers for Trafodion are available now for Linux and Windows clients. Connectivity via JDBC and ODBC is provided by Database Connectivity Services (DCS), which provides fault-tolerant access to the Trafodion cluster.


We welcome users and contributors to the Trafodion community. If you’d like to download the software, learn more about its architecture or contribute to its future development, visit our Trafodion web site.

The Unstructured Leprechaun

most data that people say is "Unstrucutred" really has structure. The key to finding the value within is to approach it a more specific method, rather than looking for a "overgeneralizing" way of doing it.

The Real-Time Unicorn

This is part one of a series I call the "de-mythification" series, wherein I'll aim to clear up some of the more widespread myths in the big data marketplace.


In the first of this multi-part series, I’ll address one of the most common myths my colleagues and I have to confront in the Big Data marketplace today: the notion of “real-time” data visibility. Whether it’s real-time analytics or real-time data, the same misconception always seems to come up. So I figured I’d address this, define what “real-time” really means, and provide readers some advice on how to approach this topic in a productive way.


First of all, let’s establish the theoretical definition of “real-time” data visibility. In the purest interpretation, it means that as some data is generated – say, a row of log data in an Apache web server – the data would immediately be queryable. What does that imply? Well, we’d have to parse the row into something readable by a query engine – so some program would have to ingest the row, parse the row, characterize it in terms of metadata, and understand enough about the data in that row to determine a decent machine-level plan for querying it. Now since all our systems are limited by that pesky “speed of light” thing, we can’t move data any faster than that – considerably slower in fact. So even if we only need to move the data through the internal wires of the same computer where the data is generated, it would take measurable time to get the row ready for query. And let’s not forget the time required for the CPU to actually perform the operations on the data. It may be nanoseconds, milliseconds, or longer, but in any event it’s a non-zero amount of time.


So “real-time” never, ever means real-time, despite marketing myths to the contrary.


There are two exceptions to this – slowing down time inside the machine, or technology which queries a stream of data as it flows by (typically called complex event processing, or CEP). With regard to the first option: let's say we wanted to make data queryable as soon as the row is generated.  We could make the flow from the logger to the query engine part of one synchronous process. So the weblog row wouldn’t actually be written until it were also processed and ready for query. Those of you who administer web and application infrastructures are probably getting gray hair just reading this as you can imagine the performance impact to a web application. So, in the real world, this is a non-starter.  The other option - CEP - is exotic and typically very expensive, and while it will tell you what's happening at the current moment, it's not designed to build analytics models.  It's largely used to put those models to work in a real-time application such as currency arbitrage.


So, given all this, what’s a good working definition of “real-time” in the world of big data analytics?


Most organizations define it this way: “As fast as it can be done providing a correct answer and not torpedoing the rest of the infrastructure or the technology budget”.


Once everyone gets comfortable with that definition, then we can discuss the real goal: reducing the time to useful visibility of the data to an optimal minimum. This might mean a few seconds, it might mean a few minutes, or it might mean hours or longer. In fact, for years now I’ve found that once we get the IT department comfortable with the practical definition of real-time, it invariably turns out that the CEO/CMO/CFO/etc. really meant exactly that when they said they needed real-time visibility to the data. So, in other words, when the CEO said “real-time”, she meant “within fifteen minutes” or something along those lines.


This then becomes a realistic goal we can work towards in terms of engineering product, field deployment, customer production work, etc. Ironically, chasing the real-time unicorn can actually impede efforts to develop high speed data flows by forcing the team to chase unrealistic targets for which, at the end of the day, there is no quantifiable business value.


So when organizations say they need “real-time” visibility to the data, I recommend not walking away from that conversation until fully understanding just what that phrase means, and using that as the guiding principle in technology selection and design.


I hope readers found this helpful! In the remaining segments of this series, I’ll address other areas of confusion in the Big Data marketplace. So stay tuned!


Next up: The Unstructured Leprechaun



Introducing Vertica "Dragline"

Today, we announced “Dragline,” the code name for the latest release of the HP Vertica Analytics Platform. Focused on the strategic value of all data to every organization, “Dragline” includes a range of industrial-strength features befitting its code name for serious Big Data initiatives.

Vertica on MapR SQL-on-Hadoop - join us in June!

Join us online on June 3 - or live at HP Discover on June 11 - to learn more about the HP Vertica MapR high-performance, tightly-integrated SQL-on-Hadoop solution.


And download the the MapR Sandbox for Hadoop - specially tuned for the HP Vertica Analytics Platform - from the HP Vertica Marketplace.

Can Vertica Climb a Tree?



The answer is YES if it is the right king of tree. Here “tree” refers to a common data structure that consists of parent-child hierarchical relationship such as an org chart. Traditionally this kind of hierarchical data structure can be modeled and stored in tables but is usually not simple to navigate and use in a relational database (RDBMS). Some other RDBMS (e.g.. Oracle) has a built-in CONNECT_BY function that can be used to find the level of a given node and navigate the tree. However if you take a close look at its syntax, you will realize that it is quite complicated and not at all easy to understand or use.


For a complex hierarchical tree with 10+ levels and large number of nodes, any meaningful business questions that require joins to the fact tables, aggregate and filter on multiple levels will result in SQL statements that look extremely unwieldy and can perform poorly. The reason is that such kind of procedural logic may internally scan the same tree multiple times, wasting precious machine resources. Also this kind of approach flies in the face of some basic SQL principles, simple, intuitive and declarative. Another major issue is the integration with third-party BI reporting tools which may often not recognize vendor-specific variants such as CONNECT_BY.


Other implementations include ANSI SQL’s recursive SQL syntax using WITH and UNION ALL, special graph based algorithms and enumerated path technique. These solutions tend to follow an algorithmic approach and as such, they can be long on theory but short on practical applications. Since SQL derives its tremendous power and popularity from its declarative nature, specifying clearly WHAT you want to get out of a RDBMS but not HOW you can get it, a fair question to ask is: Is there a simple and intuitive approach to the modeling and navigating of such kind of hierarchical (recursive) data structures in a RDBMS? Thankfully the answer is yes.


In the following example, I will discuss a design that focuses on “flattening” out such kind of hierarchical parent-child relationship in a special way. The output is a wide sparsely populated table that has extra columns that will hold the node-ids at various levels on a tree and the number of these extra columns is dependent upon the depth of a tree. For simplicity, I will use one table with one hierarchy as an example. The same design principles can be applied to tables with multiple hierarchies embedded in them. The following is a detailed outline of how this can be done in a program/script:


  1. Capture the (parent, child) pairs in a table (table_source).
  2. Identify the root node by following specific business rules and store this info in a new temp_table_1. Example: parent_id=id.
  3. Next find the 1st level of nodes and store them in a temp_table_2. Join condition:
  4. Continue to go down the tree and at the end of each step (N), store data in temp_table_N. Join condition:, where M=N+1.
  5. Stop at a MAX level (Mevel) when there is no child for any node at this level (leaf nodes).
  6. Create a flattened table: table_flat by adding in total (Mlevel+1) columns named as LEVEL, LEVEL_1_ID,….LEVEL_Mlevel_ID.
  7. A SQL insert statement can be generated to join all these temp tables together to load into the final flat table: table_flat.
  8. When there are multiple hierarchies in one table, the above procedures can be repeated for each hierarchy to arrive at a flattened table in the end.

This design is general and is not specific to any particular RDBMS architecture, row or column or hybrid. However the physical implementation of this design naturally favors columnar databases such as Vertica. Why? The flattened table is usually wide with many extra columns and these extra columns tend to be sparsely populated and they can be very efficiently stored in compressed format in Vertica. Another advantage is that when a small set of these columns are included in the select clause of an SQL, because of Vertica’s columnar nature, the other columns (no matter how many there are) will not introduce any performance overhead. This is as close to “free lunch” as you can get in a RDBMS. Let’s consider the following simple hierarchical tree structure:

Vertica Tree diagram

There are four levels and the root node has an ID of 1. Each node is assumed to have one and only one parent (except for the root node) and each parent node may have zero to many child nodes. The above structure can be loaded into a table (hier_tab) having two columns: Parent_ID and Node_ID, which represent all the (parent, child) pairs in the above hierarchical tree:

CHart 1


It is possible to develop a script to “flatten” out this table by starting from the root node, going down the tree recursively one level at a time and stopping when there is no data left (i.e. reaching the max level or depth of the tree). The final

output is a new table (hier_tab_flat):


Chart 2


What’s so special above this “flattened” table? First, this table has the same key (Node_ID) as the original table; Second, this table has several extra columns named as LEVEL_N_ID and the number of these columns is equal to the max number of levels (4 in this case) plus one extra LEVEL column; Third, for each node in this table, there is a row that includes the ID’s of all of its parents up to the root (LEVEL=1) and itself. This represents a path starting from a node and going all the way up to the root level.The power of this new “flattened” table is that it has encoded all the hierarchical tree info in the original table. Questions such as finding a level of a node and all the nodes that are below a give node, etc. can be translated into relatively simple SQL statements by applying predicates to the proper columns.


Example 1:Find all the nodes that are at LEVEL=3.Select Node_ID From hier_tab_flat Where LEVEL=3;

Example 2:Find all the nodes that are below node= 88063633.

This requires two logical steps (which can be handled in a front-end application to generate the proper SQL).

Step 2.1. Find the LEVEL of node= 88063633 (which is 3).

Select LEVEL From hier_tab_flat Where Node_ID=88063633;

Step 2.2. Apply predicates to the column LEVE_3_ID:

Select Node_ID From hier_tab_flat Where LEVE_3_ID =88063633;


By invoking the script that flattens one hierarchy repeatedly, you can also flatten a table with multiple hierarchies using the same design. With this flattened table in your Vertica tool box, you can climb up and down any hierarchical tree using nothing but SQL.



Po Hong is a senior pre-sales engineer in HP Vertica’s Corporate Systems Engineering (CSE) group with a broad range of experience in various relational databases such as Vertica, Neoview, Teradata and Oracle

HP Vertica Tutorials You Asked, We Listened.

Over recent months, we’ve heard our community request short, instructional videos and tutorials to help them learn more about the rich and powerful features of the HP Vertica Analytics Platform. We've heard you loud and clear, and have put together some videos to help you maximise your potential with HP Vertica.

Gartner Magic Quadrant Released – HP Vertica Enters the Leader’s Quadrant

Gartner released the 2014 Magic Quadrant for Data Warehouse and Database Management Systems, and we are very proud to announce that the HP Vertica Analytics Platform has entered the Leaders Quadrant!

Labels: gartner MQ

Enhancing Big Data Analytics with the HP Vertica Marketplace

HP Vertica Marketplace.jpgAt the O’Reilly Strata Conference, we demonstrated the just-announced HP Vertica Marketplace, an online destination for developers, HP Vertica users, and technology partners to create and share innovative big data analytics solutions built for the HP Vertica Analytics Platform.


Keep reading to find out what this announcement means for you.

Labels: Big Data| Vertica

Our users validate the value of Vertica

HP Vertica Software rocks.pngWe asked the questions and you answered them. We recently asked TechValidate, a trusted authority for creating customer evidence content, to survey the HP Vertica customer base.


Keep reading to find out what the survey showed and how customers are seeing the benefits of the HP Vertica Analytics Platform.

Labels: Analytics| Big Data

Welcoming Facebook to the growing family of HP Vertica customers!

FacebookDiscover-1024x576.jpgThis week at HP Discover Barcelona, we were thrilled to welcome one of our newest Vertica customers – Tim Campos, CIO of Facebook, during George Kadifa’s keynote.


Facebook selected the HP Vertica Analytics Platform as one component of its big data infrastructure.  Vertica’s value to Facebook can be found in its ability to provide business insights with incredible speed and flexibility. HP Vertica supports Facebook’s business analysts and helps the company be more productive through dramatically reduced query time. It is also valuable for providing accurate forecasting and aiding data driven decisions.


Guest post by Chris Selland, VP Marketing HP Vertica

Labels: Big Data| Vertica

HP Vertica 7, uniting and simplifying the worlds of data exploration and analysis

crane-flex-table-05-300x168.jpgToday, we launched HP Vertica 7 “Crane,” our latest release of the HP Vertica Analytics Platform.  This release is a major milestone, as not only does it deliver exciting new platform enhancements, but it also marks HP Vertica’s entry into the data exploration space. In fact, we believe HP Vertica 7 will revolutionize many organizations’ approach to data exploration. With HP Vertica 7, we are providing the most open and comprehensive SQL-based solution to storing, exploring, and serving up big data deployments that span structured and, now, semi-structured data.


Keep reading to find out what HP Vertica 7 will mean for you.


Guest Post by

Luis Maldonado

Director of Product Management

HP Vertica

Big Data analytics: what’s old is new again

Big Data Analytics.jpgMany consider Big Data analytics to be a new paradigm. In reality, the analytics of massive amounts of data has been in practice for years, particularly in the financial services, communications and manufacturing industries. Interestingly, one of the early pioneers was UPS, which used analytics in the '50s to improve operations.


Find out how companies are looking to ideas of the past to gather information for the future.


Guest Post by Kevin McConnell, Analytics Solutions & SI Alliances Global Leader at HP Software, Analytic Industry Solutions at Vertica Systems and Jeff Healey, Director of Product Marketing, HP Vertica

CIOs vs CMOs: Who’s biggest on Big Data?


Alec Wagner is an associate editor for the Discover Performance blog.


Much like BYOD and SaaS, Big Data is infiltrating enterprises by making an end-run around IT. IDC forecasts that by 2016, LOB executives will be directly involved in 80 percent of new IT investments. As vendors make inroads by selling to LOBs—and getting chief marketing officers (CMOs) to sign the contracts—Big Data initiatives are as likely to be born in marketing as they are in IT.


To find out how this new environment is changing the roles of IT and marketing/LOBs, HP Discover Performance is hosting a webcast (with an introduction from Vertica VP Chris Selland) that brings a CIO and CMO together for a frank discussion of Big Data. Who should have control? 

Labels: Big Data

How MZI HealthCare identifies big data patient productivity gems using HP Vertica

As part of ourpodcast series, Dana Gardner, president and principal analyst for Interarbor Solutions, recently conducted an interview with Greg Gootee, product manager at MZI HealthCare.   MZI HealthCare develops and provides sophisticated software solutions that are flexible, reliable, cost effective and help reduce the complexities of the healthcare industry.


Guest Post by Chuck Smith, Customer Marketing Manager, HP Vertica

Labels: Big Data| healthcare

HP Vertica in private cloud deployments-- how does it work?

Private_Cloud3.pngWhat is the role of Big Data in cloud deployments?  Is a private cloud deployment a viable option for a Big Data implementation? What are the impacts that Big Data will have on the deployment, maintenance and performance of the system?


Continue reading to find out how Big Data and cloud deployments work effectively together.


Guest post by John Margaglione, Vice President of Systems Engineering at HP Vertica

Doing everything with Big Data

chris-wegryzyn-300x172.jpgBig Data and the presidency. For the 2012 presidential election, big data played a key role in determing voter data and opinions. Chris Wegryzyn, director of Data Architecture for the Democratic National Committee presented on this topic at the HP Vertica Big Data Conference in August.


Keep reading to find out how Wegryzyn and the DNC were powered by HP Vertica and the power of Big Data 

Labels: data analytics

Harnessing All Of Your Data


Agility is a key factor in today’s Big Data landscape. Having a strategy in place to deal with the volume of data, while crucial, is only a piece of the puzzle. A Coleman Parkes Research study, commissioned by HP, revealed that barely 50 percent of survey respondents indicated that they use all sources of structured, semi-structured, and unstructured data to analyze and act. A 50 percent gap!  

Using Big Data Analytics for Stronger Customer Support

blog-img.jpgDeloitte University Press just released the key findings from the 2013 social business global executive study and research project, focused on the increasing emphasis businesses are placing on social media and why some companies are lagging behind. 


While social business has many facets, sentiment analysis and marketing intelligence is a growing trend in how we engage with customers. The report notes that “companies are connecting with dissatisfied customers before their complaints spread and providing support wherever customers gather online.”

Visualizing Big Data

HP-BigData_Infographic-solution_thumbnail.jpgIt’s no surprise that the staggering amount of information being generated by humans and machines is transforming businesses of all shapes and sizes, but sometimes visualizing the numbers can help us better understand the Big Data landscape and its many challenges. Check out this great infographic capturing the richness and complexity of Big Data by breaking it into 3 sections:

  • Big data challenges
  • Harnessing big data
  • Generating Return On Information (ROI)

Making more of your information management with OMi Management Pack for Vertica

OMi 1.pngDatabase management is huge concern for many organizations. The HP Vertica Analytics Platform has become the chosen solution when organizations are looking to get their information under control.


The HP Vertica Analytics Platform was designed for critical business applications and has built in fault tolerance and high availability. With this functionality, people might wonder if they still need a separate monitoring solution. Keep reading to find out if it is vital for your environment.


Guest post by Ian Bromehead, Sr. Product Marketing Manager

BSM Service and Operations Bridge

Data-driven decision making with the Vertica Analytics Platform

Vertica HP Discover video.pngPhysicians need access to a wealth of critical information from multiple systems in order to make life-saving decisions on a daily basis. Greg Gootee, Product Manager, MZI Healthcare, discusses how their new application, powered by the Vertica analytics platform, helps deliver better patient care through data-driven decision making.


Keep reading to find out how MZI Heathcare helps physicans make more accurate point-of-care decisions.

The results are in: HP Vertica Analytics Platform is a game changer

HP Vertica.jpgAre you curious about what the HP Vertica Analytics Platform can do for you, in your individualized environment? Now you have the opportunity to hear what other organizations have said about the platform.


The results are in from a  recent TechValidate survey as well as from a Forrester study. Keep reading to find out what these studies revealed.


Guest Post by Chuck Smith, Customer Marketing Manager, HP Vertica

Two new case studies – Yota and Kokubu

Problems with Big Data affects organizations of all sizes. Our HP Vertica Analytics Platform was designed to meet the needs of organizations--regardless of size.


Keep reading to find out how we helped create a competitive advantage for two organizations--Russia's Yota, and Japan's Kokubu.


Written by Chuck Smith, Customer Marketing Manager, HP Vertica

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  • This account is for guest bloggers. The blog post will identify the blogger.
  • For years I've been doing video and music production back and forth between Boston MA and New Orleans LA. Starting in 2010, I've began working with Vertica (now HP Vertica) in the marketing team, doing customer testimonials, product release videos, and website management. I'm fascinated by Big Data and the amazing things my badass team at HP Vertica has done and continues to do in the industry every day.
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