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Is Big Data Giving You Grief? Part 2: Anger

Is Big Data Giving You Grief? Part Two: Anger

“We missed our numbers last quarter because we’re not leveraging Big Data! How did we miss this?!”

Continuing this five part series focused on how organizations frequently go through the five stages of grief when confronting big data challenges, this post will focus on the second stage: anger.

It’s important to note that while an organization may begin confronting big data with something very like denial, anger usually isn’t far behind. As mentioned previously, very often the denial is rooted in the fact that the company doesn’t see the benefit in big data, or the benefits appear too expensive. And sometimes the denial can be rooted in a company’s own organizational inertia.

Moving past denial often entails learning – that big data is worth pursuing. Ideally, this learning comes from self-discovery and research – looking at the various opportunities it represents, casting a broad net as to technologies for addressing it, etc. Unfortunately, sometimes the learning can be much less pleasant as the competition learns big data first…and suddenly is performing much better. This can show up in a variety of ways – your competitors suddenly have products that seem much more aligned with what people want to buy; their customer service improves dramatically while their overhead actually goes down; and so on.

For better or worse, this learning often results in something that looks an awful lot like organizational “anger”. As I look back at my own career to my days before HP, I can recall more than a few all-hands meetings hosted by somber executives highlighting deteriorating financials, as well as meetings featuring a fist pounding leader or two talking about the need to change, dammit! It’s a natural part of the process wherein eyes are suddenly opened to the fact that change needs to occur. This anger often is focused at the parties involved in the situation. So, who’re the targets, and why?

The Leadership Team

At any company worth their salt, the buck stops with the leadership team. A shortcoming of the company is a shortcoming of the leadership. So self-reflection would be a natural focus of anger. How did a team of experienced business leaders miss this? Companies task leaders with both the strategic and operational guidance of the business – so if they missed a big opportunity in big data, or shot it down because it looked to costly or risky, this is often seen as a problem.

Not to let anybody off the hook, but company leadership is also tasked with a responsibility to the investors. And this varies with the type of company, stage in the market, etc. In an organization tasked with steady growth, taking chances on something which appears risky – like a big data project where the benefits are less understood than the costs – is often discouraged. Also, leaders often develop their own “playbook” – their way of viewing and running a business that works. And not that many retool their skills and thinking over time. So their playbook might’ve worked great when brand value was determined by commercial airtime, and social media was word of mouth from a tradeshow. But the types and volume of information available are changing rapidly in the big data world, so that playbook may be obsolete.

Also, innovation is as much art as science. This is something near & dear to me both in my educational background as well as career interests. If innovation was a competence that could just be taught or bought, we wouldn’t see a constant flow of companies appearing (and disappearing) across markets. We also wouldn’t see new ideas (the web! social networking!) appear overnight to upend entire segments of the economy. For most firms, recognizing the possibilities inherent in big data and acting on those possibilities represents innovation, so it’s not surprising to see that some leadership teams struggle.

The Staff

There are times when the upset over a missed big data opportunity is aimed at the staff. It’s not unusual to see a situation where the CEO of a firm asked IT to research big data opportunities, only to have the team come back and state that they weren’t worthwhile. And six months later, after discovering that the competition is eating their lunch, the CEO is a bit upset at the IT team.

While this is sometimes due to teams being “in the bunker” (see my previous post here), in my experience it occurs far more often due to the IT comfort zone. Early in my career, I worked in IT for a human resources department. The leader of the department asked a group of us to research new opportunities for the delivery of information to the HR team across a large geographic area (yeah, I’m dating myself a bit here…this was in the very early days of the web). We were all very excited about it, so we ran back to our desks and proceeded to install a bunch of software to see what it could do. In retrospect I have to laugh at myself about this – it never occurred to me to have a conversation with the stakeholders first! My first thought was to install the technology and experiment with it, then build something.

This is probably the most common issue I see in IT today. The technologies are different but the practice is the same. Ask a room full of techies to research big data with no business context and…they’ll go set up a bunch of technology and see what it can do! Will the solution meet the needs of the business? Hmm. Given the historical failure rate of large IT projects, probably not.

The Vendors

It’s a given that the vendors might get the initial blame for missing a big data opportunity. After all, they’re supposed to sell us stuff that solves our problems, aren’t they? As it turns out, that’s not exactly right. What they’re really selling us is stuff that solves problems for which their technology was built. Why? Well, that’s a longer discussion that Clayton Christensen has addressed far better than I ever could in “The Innovator’s Dilemma”. Suffice it to say that the world of computing technology continues to change rapidly today, and products built twenty years ago to handle data often are hobbled by their legacy – both in the technology and the organization that sells it.

But if a company is writing a large check every year to a vendor – it’s not at all unusual to see firms spend $1 million or more per year with technology vendors – they often expect a measure of thought leadership from that vendor. So if a company is blindsided by bad results because they’re behind on big data, it’s natural to expect that the vendor should have offered some guidance, even if it was just to steer the IT folks away from an unproductive big data science project (for more on that, see my blog post coming soon titled “That Giant Sucking Sound is Your Big Data Lab Experiment”).

Moving past anger

Organizational anger can be a real time-waster. Sometimes, assigning blame can gain enough momentum that it distracts from the original issue. Here are some thoughts on moving past this.

You can’t change the past, only the future. Learning from mistakes is a positive thing, but there’s a difference between looking at the causes and looking for folks to blame. And it’s critical to identify the real reasons the opportunity was missed instead of playing the “blame game”, as it would suck up precious time and in fact may prevent the identification of the real issue. I’ve seen more than one organization with what I call a “Teflon team” – a team which is never held responsible for any of the impacts their work has on the business, regardless of their track record. Once or twice, I’ve seen these teams do very poor work, but the responsibility has been placed elsewhere. So the team never improves and the poor work continues. So watch out for the Teflon team!

Big data is bigger than you think. It’s big in every sense of the word because it represents not just the things we usually talk about – volume of data, variety of data, and velocity of data – but it also represents the ability to bring computing to bear on problems where this was previously impossible. This is not an incremental or evolutionary opportunity, but a revolutionary one. Can a business improve its bottom line by ten percent with big data? Very likely. Can it drive more revenue? Almost certainly. But it can also develop entirely new products and capabilities, and even create new markets.

So it’s not surprising that businesses may have a hard time recognizing this and coping with it. Business leaders accustomed to thinking of incremental boosts to revenue, productivity, margins, etc. may not be ready to see the possibilities. And the IT team is likely to be even less prepared. So while it may take some convincing to get the VP of Marketing to accept that Twitter is a powerful tool for evaluating their brand, asking IT to evaluate it in a vacuum is a recipe for confusion.

So understanding the true scope of big data and what it means for an organization is critical to moving forward.

A vendor is a vendor. Most organizations have one or more data warehouses today, along with a variety of tools for the manipulation, transformation, delivery, analysis, and consumption of data. So they will almost always have some existing vendor relationships around technologies which manage data. And most of them will want to leverage the excitement around big data, so will have some message along those lines. But it’s important to separate the technology from the message. And to distinguish between aging technology which has simply been rebranded and technology which can actually do the job.

Also, particularly in big data, there are “vendorless” or “vendor-lite” technologies which have become quite popular. By this I mean technologies such as Apache Hadoop, Mongodb, Cassandra, etc. These are often driven less by a vendor with a product goal and more by a community of developers who cut their teeth on the concept of open-source software which comes with very different business economics. Generally without a single marketing department to control the message, these technologies can be associated with all manner of claims regarding capabilities – some of which are accurate, and some which aren’t. This is a tough issue to confront because the messages can be conflicting, diffused, etc. The best advice I’ve got here is – if an open source technology sounds too good to be true, it very likely is.

Fortunately, this phase is a transitional one. Having come to terms with anger over the missed big data opportunity or risk, businesses then start to move forward…only to find their way blocked. This is when the bargaining starts. So stay tuned!

Next up: Bargaining “Can’t we work with our current technologies (and vendors)? …but they cost too much!”

Physical Design Automation in the HP Vertica Analytic Database

Automatic physical database design is a challenging task. Different customers have different requirements and expectations, bounded by their resource constraints. To deal with these challenges in HP Vertica, we adopt a customizable approach by allowing users to tailor their designs for specific scenarios and applications. To meet different customer requirements, any physical database design tool should allow its users to trade off query performance and storage footprint for different applications.

In this blog, we present a technical overview of the Database Designer (DBD), a customizable physical design tool that primarily operates under three design policies:

  • Load-optimized –DBD proposes the minimum required set of super projections (containing all columns) that permit fast load and deliver required fault tolerance.
  • Query-optimized –DBD may propose additional (possibly non-super) projections such that all workload queries are fully-optimized
  • Balanced—DBD proposes projections until it reaches the point where additional projections do not bring sufficient benefits in query optimization.

These options allow users to choose to trade off query performance and storage footprint, while considering update costs. These policies indirectly control the number of projections proposed to achieve the desired balance among query performance, storage and load constraints.
In real-world environments, query workloads often evolve over time. A projection that was helpful in the past may not be relevant today and could be wasting space or slowing down loads. This space could instead be reused to create new projections that optimize current workloads. To cater to such workload changes, DBD operates in two different modes:

  • Comprehensive–DBD creates an entirely new physical design that optimizes for the current workload while retaining parts of the existing design that are beneficial and dropping parts that are non-beneficial
  • Incremental– Customers can optionally create additional projections that optimize new queries without disturbing the existing physical design. Customers should use the incremental mode when workloads have not changed significantly. With no input queries, DBD optimizes purely for storage and load purposes.

ram_comprehensiveMode

The key challenges involved in the projection design are picking appropriate column sets, sort orders, cluster data distributions and column encodings that optimize query performance while reducing space overhead and allowing faster recovery. The DBD proceeds in two major sequential phases. During the query optimization phase, DBD chooses projection columns, sort orders, and cluster distributions (segmentation) that optimize query performance. DBD enumerates candidate projections after extracting interesting column subsets by analyzing query workload for predicate, join, group-by, order-by and aggregate columns. Run length encoding (RLE) is given special preference for columns appearing early in the sort order, because it is beneficial for both query performance and storage optimization. DBD then invokes the query optimizer for each workload query and presents a choice of the candidate projections. The query optimizer evaluates the query plans for all candidate projections, progressively narrowing the set of candidates until a stopping condition (based on the design policy) is reached. Query and table filters are applied during this process to filter one or more queries that are sufficiently optimized by chosen projections or tables that have reached a target number of projections set by the design policy. DBD’s direct use of the optimizer’s cost and benefit model guarantees that it remains synchronized as the optimizer evolves over time.

ram_inputParameters

During the storage optimization phase, DBD finds the best non-RLE column encoding schemes that achieve the smallest storage footprint for the designed projections via a series of empirical encoding experiments on the sample data. In addition, DBD creates the required number of buddy projections containing the same data but distributed differently across the cluster, enabling the design to be tolerant to node-down scenarios. When a node is down, buddy projections are employed to source the missing data in the down nodes. In HP Vertica, identical buddy projections (with same sort orders and column encodings) enable faster recovery by facilitating direct copy of their physical storage structures and DBD automatically produces such designs.

When DBD is invoked with an input set of workload queries, the queries are parsed and useful query meta-data is extracted (e.g., the predicate, group-by, order-by, aggregate and join query columns). Design proceeds in iterations. In each iteration, one new projection is proposed for each table under design. Once an iteration is done, queries that have been optimized by the newly proposed projections are removed, and the remaining queries serve as input to the next iteration. If a design table has reached its targeted number of projections (decided by the design policy), it is not considered in future iterations to ensure that no more projections are proposed for it. This process is repeated until there are no more design tables or design queries are available to propose projections for.

To form the complete search space for enumerating projections, we identify the following design features in a projection definition:

  • Feature 1: Sort order
  • Feature 2: Segmentation
  • Feature 3: Column encoding schemes
  • Feature 4: Column sets (select columns)

We enumerate choices for features 1 and 2 above, and use the optimizer’s cost and benefit model to compare and evaluate them (during the query optimization phase ). Note that the choices made for features 3 and 4 typically do not affect the query performance significantly. The winners decided by the cost and benefit model are then extended to full projections by filling out the choices for features 3 and 4, which have a large impact on load performance and storage (during the storage optimization phase).
In summary, the HP Vertica Database Designer is a customizable physical database design tool that works with a set of configurable input parameters that allow users to trade off query performance, storage footprint, fault tolerance and recovery time to meet their requirements and optionally override design features.

Building Bridges with Gumdrops and Toothpicks

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In June 2014, the HP Vertica summer interns headed to the East End House in East Cambridge, MA to work with students through a community service project. Sarah Perkins, a business planner on the Project Management team, organized the project. Since 1875, the East End House has offered innovative programs to the community and continues to strive for excellence. Their programs help support families and individuals through curricula that enhance education standards. Programming supports the whole family with comprehensive services like a Food Pantry, Community Workshops, Parent Education and Senior Programming.

The interns, accompanied by mentors Sarah Lemaire and Jaimin Dave, helped students build bridges with very limited materials: fifty gumdrops and one hundred toothpicks! The goal was to build a bridge that spanned a six-inch gap and would hold at least 300 grams, or 120 pennies.

Teams of interns paired up to work with four students, ranging from third to eighth grade. They watched and assisted as the students discussed strategies, drew prototypes and started to build and re-build their structures. As the students worked on their bridges, they got to know more about the Vertica interns, including their majors, hometowns, and the projects they are working on for the summer. Throughout the course of the day, if students could correctly answer questions about their interns, they would win HP Vertica swag, including toy bulldozers, stress balls, flashlights, and more.

Once the bridges were built, the interns and students tested them across a six-inch gap. Students placed pennies on a paper plate on top of the bridge, one-by-one, until the bridge collapsed under the weight. The winning team’s bridge, led by interns Swikriti Jain and Jun Yin, held 255 pennies, which weigh more than 1 1/3 pounds! The top two teams won a bundle of HP Vertica swag, including t-shirts, water bottles, and baseballs.

The HP Vertica interns had a great time learning about students at the East End House, and helping them build successful bridges. It was a unique opportunity to interact with students of many ages, while also encouraging them to remain active in school and participate in extracurricular activities.

Can Big Data Analytics Save Our World?

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If you ask Conservation International this question, they may just say yes. After all, Conservation International has teamed up with HP Earth Insights to provide organizations around the world — from environmentalists to policy makers – with a real-time look at what is happening within our planets most valuable natural resource: the rain forest.

But how does their work relate to you as a start-up organization or a Fortune 500 company?
First, they have surprisingly similar analytical needs to many other start-ups and corporations, collecting data regularly from 16 sites around the globe, performing more than 4 million climate measurements as of this February, and managing more than 3 TB of biodiversity information. As the name implies, this information is incredibly, well… diverse, including everything from photos to hand-recorded measurements to weather station and camera trap imagery. While your company may not be recording/analyzing the metadata of candid photos of elephants and/or chimpanzees, chances are, many of you out there are working with at least more than one type of data.

Collecting and Analyzing Multiple Data Types
All of these different data types have to be funneled into a database, analyzed, and then acted on. Running queries based on millions of climate readings begins to look a lot like doing the same on a diverse customer base like many other companies deal with every day. Many agricultural companies collect sensor data from across their farm lands to get a forecast of how the climate has affected their crops for the upcoming year. These days, utilities companies are launching Advanced Metering Infrastructures (AMI) to deal with the staggering amounts of sensor data collected from the energy usage of millions of homes. HP Vertica coincidentally works as an effective Meter Data Management (MDM) system (read more here).

Visualizing the Data and Reaching More People
Working with HP, Conservation International has built from the ground up their own analytics system and dashboard for visualizing their data from all 16 rainforests around the globe. CI DBA’s discover trends based on over 140 million simulations, and analyze the metadata from over 1.7 million photos. Not only is their custom interface intuitive, it also enables them to generate PDFs instantly and share to social media directly from the dashboard. For CI, this means more people now see more of their impact in more places to proactively address environment threats. For you, it might mean anything from less time spent prepping your data to present to management, or just simply fewer emails to send.

The Power of Prediction for the Greater Good
Like many companies, CI uses standard methodology in processing their data, and uses R for their analysis, as is very common in scientific studies. Using R, CI can proactively assess where the future trouble spots will be, and what parts of their monitored ecosystems are most threatened. Many other HP Vertica customers use R in surprisingly similar ways, such as seeing what neighborhoods a future power outage might affect most, or how serious the next year’s dry season will be to a farmer’s crops

See Conservation International at the HP Vertica Big Data Conference
These are just a few examples of how an incredibly unique organization uses HP Vertica to analyze unique data, yet does it in ways that many other groups might find surprisingly familiar. Sometimes after a closer look, we can see that many organizations have a lot more in common with their data needs than they may think, and HP Vertica is the right tool for the job.

Be sure to attend out upcoming Big Data Conference in Boston MA, where Conservation International is leading the hackathon!

Tech Support Series: Optimizing for Deletes

This blog is just the first in a series that addresses frequently asked tech support questions. For now, we’ll talk about optimizing your database for deletion.

You may find that from time to time your recovery and query execution is slow due to high volumes of delete vectors. Occasionally, performing a high number of deletes or updates can negatively affect query performance and recovery due to delete replay.

Delete replay occurs when ROS containers are merged together. The data marked for deletion in each of the ROS containers needs to be remarked once the containers are merged. This process can hold up your ETL processes because the Tuple Mover lock (T lock) stays on until the replay deletes finish.

Luckily, optimizing your database for deletes can help speed up your processes. If you expect to perform a high number of deletes, first consider the reason for deletion. The following is a list of common reasons for high delete usage:

  • You regularly delete historical data and upload new data at specific intervals
  • You constantly update data or you want to delete data that was loaded my mistake
  • You often delete staging tables

To optimize your database for deletion, follow the suggestions that correspond to your reason for deletion.

  1. If you regularly delete historical data to make room for newer data, use partitioning to chunk data into groups that will be deleted together. For example, if you regularly delete the previous month’s data, partition data by month. When you use partitioning, you can use the DROP_PARTITION function to discard all ROS containers that contain data for the partition. This operation removes historical data fast because no purging or replay deletes are involved.
  2. You may also want to delete a high volume of data because it was loaded by mistake or because you frequently update data (which involves frequently deleting data). In these cases, you may see a high volume of delete vectors. There are three good ways to prevent this:
  3.  

    1. Create delete-optimized projections by using a high cardinality column at the end of the sort order. This helps the replay delete process quickly identify rows to be marked for deletion.
    2.  

    3. Make sure your Ancient History Mark (AHM) is advancing and close to the Last Good Epoch (LGE) or Current Epoch. You may also want to periodically use the MAKE_AHM_NOW function to advance the ancient history mark to the greatest allowable value. When a mergeout occurs, all data that is marked for deletion before the AHM will be purged, minimizing the amount of replay deletes.
    4.  

    5. Periodically check the number of delete vectors in your tables using the DELETE_VECTORS system table. The automatic Tuple Mover will eventually purge deleted data but if you find your tables have a large number of delete vectors, you can manually purge records using the PURGE_TABLE function.
  4. You may find that you frequently delete staging tables. To streamline this process, you can truncate the staging table instead of deleting it using the TRUNCATE TABLE function. Truncating a table will discard the ROS containers that contain the data instead of creating delete vectors, and thus is more efficient than table deletion.

 
Frequently deleting data is often a cause of slow query performance. Fortunately, you can optimize your database for deletions with these tips and avoid the headache.

How to:

Drop a partition:

=> SELECT DROP_PARTITION (table_name, partition_value);

Get epoch:

=> SELECT current_epoch, ahm_epoch, last_good_epoch FROM SYSTEM;

Set AHM to greatest allowable value:

=> SELECT MAKE_AHM_NOW();

Determine number of delete vectors:

=> SELECT * FROM v_monitor.DELETE_VECTORS;

Purge data:

=> SELECT PURGE_TABLE(table_name);

Introducing HP 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.

Our data sheet provides detailed feature descriptions on this release’s full range of capabilities and benefits, so we’ll just focus on three top features that are sure to capture your attention (after all, they came highly requested from our growing customer base).

By the way, learn about these and all of the new “Dragline” features in our upcoming Webinar.


“Project Maverick” – Speed in All Directions

Danger Zone

Speed is a given … but what about in every possible direction? “Project Maverick” has multiple elements including fast, self-service analytics about discrete individuals or devices. Live Aggregate Projections, a key, new feature of “Project Maverick,” accelerates the speed and performance of these live lookups by up to 10x – more frequent or highly concurrent queries on an individual customer basis — by computing metrics on the data as it arrives for targeted and personalized analytics without programming accelerator layers.

For telecommunications companies as well as utilities and energy providers who, due to deregulation, are pursuing smart metering initiatives to differentiate from competitors, this capability is hot. With Live Aggregate Projections, these providers can deliver smart metering reports that educate their customers on consumption rates vis a vis their neighbors in promoting a greener planet and establishing a tighter relationship with their provider. Interested in learning how HP Vertica easily chews through the enormous volume and scale of smart meter data and the Internet of Things? Check out our newly published white paper, including detailed benchmarks.


Mixed Dynamic Workload Management – Make ’em All Happy

Coffee

Another major advancement of “Project Maverick” is Dynamic Mixed Workload Management. Commonly found in many data warehouse technologies and requested by some of our largest customers, this powerful new feature identifies and adapts to varying query complexities — simple and ad-hoc queries as well as long-running advanced queries — and dynamically assigns the appropriate amount of resources to meet the needs of all data consumers.

So, now, you can preserve your precious hardware and system resources, while pleasing even your most demanding internal and external constituents – from analysts who seek ad-hoc insights to data scientists who manage complex analytics to business executives who need customized views or dashboards on Monday mornings.


Cost-Optimized Storage – Don’t Break the Bank

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Hadoop is commonly used as a data lake to store bulk data. That’s why with each release, we offer tighter integration that delivers the most open SQL on Hadoop. “Dragline” builds on that strong heritage by helping you to manage multiple storage tiers for cost-effective analytics. You can perform advanced SQL queries on bulk data stored in HDFS using the HP Vertica Analytics Platform without moving the data or using any connectors. You can move data into HP Vertica when your organization requires faster performance in-depth analytics.

HP Vertica supports all of the major Hadoop distributors but the power of MapR and HP Vertica on the same cluster is something special. Join our upcoming Webinar with MapR to get the full details behind this solution and to build your business case for SQL on Hadoop.

Try HP Vertica 7 today and stay tuned for more blog posts and materials to support this release.

Work hard, have fun and make a difference!

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My name is Jaimin and I work as a Software Engineer in the Distributed Query Optimizer Team at HP Vertica. I wanted to share with you what I think makes Vertica the best place to work! I will explain the kind of impact you can make as an employee/intern at HP Vertica, while sharing my personal experiences.

As a student, I researched many companies I might want to work for to get a better understanding of the everyday life of software engineers. However, what I was most interested in learning about was the kinds of things engineers might do that went above and beyond the normal day-to-day stuff.

Is writing code something unique to the job?

No! Right?

As Software Engineers, we write code, develop algorithms, and implement them. But here at HP Vertica, we do lots of other things besides simply writing code.

Go above and beyond!

Vertica is different from other companies as far as normal day-to-day stuff goes.

Let me ask you this question: How many new graduates would you guess could get a chance to file a patent within their first 6 months of joining a company? How many would get chance to write a paper within first six months? Not a lot, right?

In my experience at HP Vertica, I’ve seen that just about all new graduate engineers file at least one patent in their first year at work. This speaks to the fact that the work we do here at Vertica is completely innovative. Our projects have a huge business impact.

Be the captain of your ship!

Vertica offers engineers incredible opportunities! All you have to do is be willing to accept them. One of the best things about HP Vertica is that you work in an environment where other engineers are smarter than you! You’ll find yourself constantly challenged to learn new, interesting, and exciting things. You’ll get better exposure and, more importantly, you have a massive role to play in the company’s growth and development.

Something else that’s unique about HP Vertica—the projects you work on as an intern become part of the shipping product! As a result, you’ll get the chance to see your code in action and sometimes you can learn what customers have to say about your feature in particular. You won’t be allowed to sit idle for a minute because we have a very short release cycle. This will keep you on your toes and encourage you to think something new day in and day out.

Here, engineers are not forced to work on this and that—they have a great deal of autonomy and frequently get to choose the things they work on. If you have an idea you think can help improve the product, you are encouraged to see it through. And, you’ll also get a chance to participate in various technical events that take place within HP and submit your ideas.

Taking initiative is always encouraged and you’ll be expected to make, discuss, and defend your design decisions with your mentors instead of just following directions. You’ll also be able to learn about the complexities of building a database and how we achieve the performance advantages in HP Vertica.

It is also easy to move between the teams. It is entirely up to you and the only question is what you want to do?

Share and gain knowledge!

Knowledge Sharing is another important thing at Vertica. We do a lunch talk where we discuss any new paper related to database systems. Every now and then people from various teams give tech talks so that each team is aware of what people in other groups are doing.

As a fresh graduate before joining Vertica, I did not have any experience working on a database optimizer product, though I had worked a bit on optimizations when I took a compiler class. Because of the great culture and environment at Vertica, I didn’t find the transition difficult at all. Sometimes it was challenging, but it allowed me to learn a lot by working with incredibly smart people at the company while working on challenging projects (I wonder how many people have the opportunity to work on the design and implementation of queries involving Set Operators during their first year of work).

Have fun!

We frequently unwind doing fun things at work, including watching the Olympics games or other sporting events during lunch, or playing table-tennis and board games when we can. Vertica provides a lot of flexibility and it comes with huge responsibility. You’re expected to get your work done on time—if you do that, no one will have any problem with having a little fun. Interns also go on outdoor field trips, including horseback riding, hiking to Blue Hills, going for a movie, participating in a bocce tournament, and water activities such as motor boat racing. Once, we went to the Boston Harbor and tried to learn how to sail a boat from one of our in-house experts in Vertica.

We are looking for people to join Vertica! Do you have any interest in being challenged in an innovative design environment? Then apply today!

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