Tagged: love Toggle Comment Threads | Keyboard Shortcuts

  • admin 9:56 am on April 22, 2017 Permalink
    Tags: , love   

    We love SAP (data) ! 

    Latest imported feed items on Analytics Matters

     
  • admin 9:51 am on February 2, 2016 Permalink
    Tags: , , , love, , , Valentine’s   

    5 Valentine’s Day Marketing Ideas : How to Make People Fall in Love With Your Brand 

    Ahh, it’s that time of year again: my Facebook feed is swamped with hearts and clichés.

    What is it about Valentine’s Day that makes us all so emotional? That makes us publicly announce our deepest and warmest feelings towards our loved ones?

    In some way or another we all want to love and feel loved. Not only on Valentine’s Day, but every day.

    Emotions help us connect. They help us inspire

    Do you want your brand to be able to connect and inspire with your customers? Here are 5 Valentine’s Day marketing ideas to help get you started.

    1. Make them feel special.

    When the sparks are flying in a relationship both sides feel the mushy side of love. Making your partner feel special and loved is what puts a fire in your relationship. As a brand you can show your love in a few different ways:

    1. Send out individualized messages.

    As a relationship grows, so does ones knowledge of their partners likes and dislikes. Just like you would not treat your current relationship like previous ones, this applies with customers too. Send email campaigns and push notifications that are personalized and make the customer feel important and valued.

    2. Make them laugh

    Why did the marketing couple decide not to get married?

    This was because they weren’t on the same landing page.

    Humor is always a wonderful way to connect with people. Make them laugh or smile and they will come back for more. As it’s said, “laughter is the best way to make somebody’s heart beat”.

    3. Send them love notes and poetry.

    That’s right you heard me. Brands can also send their customer poetry and show them how much they care on Valentine’s Day. In fact, last year Starbucks did that just.

    Strabucks cofee

    Source: SocialBro

    2. Surprise them

    We all love gifts and gestures, especially when we don’t expect them. The bigger the gesture is, the better we feel!

    A few ideas for surprises and gifts that will make your readers happy:

    1.Special limited time offers.

    2.Offer a free gift.

    Offer a gift

    Source: Windsor Circle

    3. Discounts online or in-store.

    4. Run a Valentine’s Day contest resulting in the winner getting a wonderful prize.

    5. Offer free shipping.

    Offer free shipping

    Source: Windsor Circle

    3. Take an active role in the relationship

    Just like a partner expects you be an active, your customers expect it too. Give them feedback and guide them through the buyer journey. If they forget an item in their cart send them a loving reminder. If they bought an item and you know that another item would be a perfect match to complete the purchase, let them know and they will appreciate it.

    4. Innovate

    Like in any relationship, don’t expect your customers to stick around if you keep doing the same things. Be creative, excite them and stand out from your competitors in order to keep your audience entertained and intrigued. Keep them on their toes and excited to see what your brand is up to this time. Take Scribbler for instance. They used their blog to have customers share their definition of love. Via your Scribbler account you were able to tweet your answer. This simple act was a great way to generate leads and engage their customers in a fun and interactive way.

    Marketing iteractive

    Source: HubSpot

    5. Know Your Partner

    Just like in dating, the more you know about someone, the better you will be at pleasing them. Take into account past behavior and make your marketing campaigns tailored to the users’ preferences. Take a look at last year and see what customers have purchased in the past and use this data to target those customers correctly.

    Did you love this post? Then share it to make me feel the love. Who knows – you might end up getting a surprise from us like a free trial offer of our services.

     

    The post 5 Valentine’s Day Marketing Ideas : How to Make People Fall in Love With Your Brand appeared first on Teradata Applications.

    Teradata Blogs Feed

     
  • admin 9:51 am on June 26, 2015 Permalink
    Tags: love,   

    Why We Love Presto 

    Concurrent with acquiring Hadoop companies Hadapt and Revelytix last year, Teradata opened the Teradata Center for Hadoop in Boston. Teradata recently announced that a major new initiative of this Hadoop development center will include open-source contributions to a distributed SQL query engine called Presto. Presto was originally developed at Facebook, and is designed to run high performance, interactive queries against Big Data wherever it may live — Hadoop, Cassandra, or traditional relational database systems.

    Among those people who will be part of this initiative and contributing code to Presto include a subset of the Hadapt team that joined Teradata last year. In the following, we will dive deeper into the thinking behind this new initiative from the perspective of the Hadapt team. It is important to note upfront that Teradata’s interest in Presto, and the people contributing to the Presto codebase, extends beyond the Hadapt team that joined Teradata last year. Nonetheless, it is worthwhile to understand the technical reasoning behind the embrace of Presto from Teradata, even if it presents a localized view of the overall initiative.

    Around seven years ago, Ashish Thusoo and his team at Facebook built the first SQL layer over Hadoop as part of a project called Hive. At its essence, Hive was a query translation layer over Hadoop: it received queries in a SQL-like language called Hive-QL, and transformed them into a set of MapReduce jobs over data stored in HDFS on a Hadoop cluster. Hive was truly the first project of its kind. However, since its focus was on query translation into the existing MapReduce query execution engine of Hadoop, it achieved tremendous scalability, but poor efficiency and performance, and ultimately led to a series of subsequent SQL-on-Hadoop solutions that claimed 100X speed-ups over Hive.

    Hadapt was the first such SQL-on-Hadoop solution that claimed a 100X speed-up over Hive on certain types of queries. Hadapt was spun out of the HadoopDB research project from my team at Yale and was founded by a group of Yale graduates. The basic idea was to develop a hybrid system that is able to achieve the fault-tolerant scalability of the Hive MapReduce query execution engine while leveraging techniques from the parallel database system community to achieve high performance query processing.

    The intention of HadoopDB/Hadapt was never to build its own query execution layer. The first version of Hadapt used a combination of PostgreSQL and MapReduce for distributed query execution. In particular, the query operators that could be run locally, without reliance on data located on other nodes in the cluster, were run using PostgreSQL’s query operator set (although Hadapt was written such that PostgreSQL could be replaced by any performant single-node database system). Meanwhile, query operators that required data exchange between multiple nodes in the cluster were run using Hadoop’s MapReduce engine.

    Although Hadapt was 100X faster than Hive for long, complicated queries that involved hundreds of nodes, its reliance on Hadoop MapReduce for parts of query execution precluded sub-second response time for small, simple queries. Therefore, in 2012, Hadapt started to build a secondary query execution engine called “IQ” which was intended to be used for smaller queries. The idea was that all queries would be fed through a query-analyzer layer before execution. If the query was predicted to be long and complex, it would be fed through Hadapt’s original fault-tolerant MapReduce-based engine. However, if the query would complete in a few seconds or less, it would be fed to the IQ execution engine.

    presto graphic blogIn 2013 Hadapt integrated IQ with Apache Tez in order avoid redundant programming efforts, since the primary goals of IQ and Tez were aligned. In particular, Tez was designed as an alternative to MapReduce that can achieve interactive performance for general data processing applications. Indeed, Hadapt was able to achieve interactive performance on a much wider-range of queries when leveraging Tez, than what it was able to achieve previously.

    Figure 1: Intertwined Histories of SQL-on-Hadoop Technology

    Unfortunately Tez was not quite a perfect fit as a query execution engine for Hadapt’s needs. The largest issue was that before shipping data over the network during distributed operators, Tez first writes this data to local disk. The overhead of writing this data to disk (especially when the size of the intermediate result set was large) precluded interactivity for a non-trivial subset of Hadapt’s query workload. A second problem is that the Hive query operators that are implemented over Tez use (by default) traditional Volcano-style row-by-row iteration. In other words, a single function-invocation for a query operator would process just a single database record. This resulted in a larger number of function calls required to process a large dataset, and poor instruction cache locality as the instructions associated with a particular operator were repeatedly reloaded into the instruction cache for each function invocation. Although Hive and Tez have started to alleviate this issue with the recent introduction of vectorized operators, Hadapt still found that query plans involving joins or SQL functions would fall back to row-by-row iteration.

    The Hadapt team therefore decided to refocus its query execution strategy (for the interactive query part of Hadapt’s engine) to Presto, which presented several advantages over Tez. First, Presto pipelines data between distributed query operators directly, without writing to local disk, significantly improving performance for network-intensive queries. Second, Presto query operators are vectorized by default, thereby improving CPU efficiency and instruction cache locality. Third, Presto dynamically compiles selective query operators to byte code, which lets the JVM optimize and generate native machine code. Fourth, it uses direct memory management, thereby avoiding Java object allocations, its heap memory overhead and garbage collection pauses. Overall, Presto is a very advanced piece of software, and very much in line with Hadapt’s goal of leveraging as many techniques from modern parallel database system architecture as possible.

    The Teradata Center for Hadoop has thus fully embraced Presto as the core part of its technology strategy for the execution of interactive queries over Hadoop. Consequently, it made logical sense for Teradata to take its involvement in the Presto to the next level. Furthermore, Hadoop is fundamentally an open source project, and in order to become a significant player in the Hadoop ecosystem, Teradata needs to contribute meaningful and important code to the open source community. Teradata’s recent acquisition of Think Big serves as further motivation for such contributions.

    Therefore Teradata has announced that it is committed to making open source contributions to Presto, and has allocated substantial resources to doing so. Presto is already used by Silicon Valley stalwarts Facebook, AirBnB, NetFlix, DropBox, and Groupon. However, Presto’s enterprise adoption outside of silicon valley remains small. Part of the reason for this is that ease-of-use and enterprise features that are typically associated with modern commercial database systems are not fully available with Presto. Missing are an out-of the-box simple-to-use installer, database monitoring and administration tools, and third-party integrations. Therefore, Teradata’s initial contributions will focus in these areas, with the goal of bridging the gap to getting Presto widely deployed in traditional enterprise applications. This will hopefully lead to more contributors and momentum for Presto.

    For now, Teradata’s new commitments to open source contributions in the Hadoop ecosystem are focused on Presto. Teradata is only committing to contribute a small amount of Hadapt code to open source — in particular those parts that will further the immediate goal of transforming Presto into an enterprise-ready, easy-to-deploy piece of software. However, Teradata plans to monitor Presto’s progress and the impact of Teradata contributions. Teradata may ultimately decide to contribute more parts of Hadapt to the Hadoop open source community. At this point it is too early to speculate how this will play out.

    Nonetheless, Teradata’s commitment to Presto and its commitment to making meaningful contributions to an open source project is an exciting development. It will likely have a significant impact on enterprise-adoption of Presto. Hopefully, Presto will become a widely used open source parallel query execution engine — not just within the Hadoop community, but due to the generality of its design and its storage layer agnosticism, for relational data stored anywhere.

    ==================================================================================

    daniel abadi crop BLOG bio mgmtDaniel Abadi is an Associate Professor at Yale University, founder of Hadapt, and a Teradata employee following the recent acquisition. He does research primarily in database system architecture and implementation. He received a Ph.D. from MIT and a M.Phil from Cambridge. He is best known for his research in column-store database systems (the C-Store project, which was commercialized by Vertica), high performance transactional systems (the H-Store project, commercialized by VoltDB), and Hadapt (acquired by Teradata). http://twitter.com/#!/daniel_abadi.

    The post Why We Love Presto appeared first on Data Points.

    Teradata Blogs Feed

     
  • admin 10:03 am on April 11, 2015 Permalink
    Tags: love, , , , ,   

    Marketers Love Video — But Are They Maximizing Its Potential? 

    video reelYouTube officially hit the internet in 2005, and the video landscape hasn’t been the same since. It didn’t take long for other video-sharing sites like Vimeo and Dailymotion to crop up, and now there are platforms specific to professional and amateur humor (Funny or Die, Cracked, Break.com), music (Vevo, the always-evolving MySpace), viral videos (UpWorthy, Buzznet, Buzzfeed), video streaming of television and movies (Hulu, Netflix), live-streaming (VideoLAN, Ustream, LiveStream), short-form video apps (Vine)…

    Plus, there are photo-sharing sites with video capabilities (Instagram, Photobucket, SmugMug), personal and group video messaging apps (SnapChat), social media websites with video functionality (Twitter, Facebook, blogs)…

    And just when you think you’ve got a solid list going, others launch. Whew!

    On top of all that, each of these (somewhat loosely defined) categories has multiple players, and they’re constantly shifting, too. Big companies buy up the little ones, small divisions spin-off, related sites cross-promote, and all of them are doing their best to capture the eyes and ears of video-hungry viewers.

    Entrepreneurs and investors love video because of its tremendous appeal. People of all ages and backgrounds the world over love to view and share video online, whether it’s the latest “You gotta see this!” clip making the rounds, re-runs of a favorite, old television show or (like it or not) anything to do with cats.

    Brands love video as a storytelling tool, too —although the saturation of the space certainly ups the ante for video marketing campaigns. How can you know what will engage your customers? What will they comment on and share? And adding to the complexity, how does video advertising fit in? You can use pre-roll on video-centric websites, “native advertising” to integrate with your site’s overall content, more traditional online ads, or a combination of them all. But you need to find just the right balance because most people don’t go online looking for a pitch – they want value and entertainment first and foremost.

    So, perhaps it’s no surprise that marketers and advertisers are searching for ways to optimize and manage video advertising and marketing. If you’ve been wondering about it yourself, my advice is simple:

    The best way to make a video connection, or any kind of marketing connection these days, is to get individualized.

    Your video content needs to be both meaningful and compelling to your customer, and it needs to be wherever that customer is already consuming video, or where he/she might be open to a video pitch from you, such as a blog or article site related to your product or service. If your content isn’t relevant, it will become a part of the “video noise” online users experience each day. And if your content isn’t where your customers is, well, you’re playing to an empty house.

    So how do you know what each customer finds relevant? How do you know where to reach them? Your data holds the key.

    With data driven marketing, you can use the wealth of information available to you about your customer behaviors—across a wide range of sites and channels—to gain insight. You can better understand where they spend time, where they’re most likely to engage and what types of content they click on most often. You can learn what they’re saying about your industry or your products, what they consider concerns or pain points and what deals, discounts and offers compel them to make a purchase. When you have information like that in-hand, you can deliver the kind of video content and advertising that your customers will care about – instead of just clicking away.

    Yes, video is hot right now, and video content and advertising can be a real boon for brands, provided you have the digital asset management tools you need to create, manage and deliver rich and more relevant media content to your customers and prospects.

    The post Marketers Love Video — But Are They Maximizing Its Potential? appeared first on Teradata Applications.

    Teradata Blogs Feed

     
c
Compose new post
j
Next post/Next comment
k
Previous post/Previous comment
r
Reply
e
Edit
o
Show/Hide comments
t
Go to top
l
Go to login
h
Show/Hide help
shift + esc
Cancel