Author
Kevin White
Kevin is on the marketing team and is responsible for marketing operations and content strategy.
Case Studies
Cisco UCS and SingleStore: Real-Time Data Warehouse for the Enterprise
Real-time analytics on live data and the ability to analyze data at scale, are key for any digital organization. Acting on insights in the moment helps deliver contextual user experiences, identify new sources of revenue, and prevent costly expenditures.
To become a responsive data-driven business, organizations must address current data latency challenges. These challenges are commonly found across three general areas:
Three Data Latency Challenges and Solutions
Slow data loading: Loading data, moving past batch processing, and receiving analytics responses in real time remains out of reach for too many businesses. A real-time data warehouse reduces extract, transform, and load (ETL), combining transactional and analytical workloads into a single system.
Lengthy query execution: Operational insights must be readily available for in-the-moment decisions. A data warehouse that delivers a fast query response can deliver insights whenever the application or users require it, ultimately providing a differentiated service or identifying opportunities.
Low concurrency: Digital business assumes a large-scale use of data across an entire business or customer base. As more users engage and interact with data, the response time for those interactions must maintain a consistent experience. A scalable data warehouse will help ensure that data and user growth will not negatively affect the operational system.
Real-Time Data Warehouse for the Enterprise
SingleStore on Cisco UCS Integrated Infrastructure for Big Data and Analytics is built to address these challenges.
SingleStore provides a scalable, real-time data warehouse platform for high-performance applications that require fast, accurate, secure, and always available data. SingleStore scales linearly to millions of events per second while analyzing petabytes of data for insights. SingleStore also enables fast ingestion and concurrent analytics needed for sensor systems, recommendation systems, and use cases that require instant, actionable insights.
Learn more about how we are working with Cisco to deliver real-time analytics to the enterprise. Download the full Cisco UCS solution brief here. Check out Raghunath Nambiar’s (Cisco UCS CTO) blog post here: https://blogs.cisco.com/datacenter/memsql
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Machines and the Magic of Fast Learning (Strata Keynote Recording)
How can big data and machine learning be used for good?
Updated Dec. 12, 2019, including adding a transcript. This blog post discusses the use of SingleStore as a database for machine learning and artificial intelligence. See our recent Thorn case study for the latest on SingleStore’s work with Thorn, including Thorn’s use of SingleStoreDB Cloud.
In our keynote at Strata+Hadoop World, SingleStore CEO Eric Frenkiel shared how we are working with Thorn to provide a new approach to machine learning and real-time image recognition to combat child exploitation.
About Thorn
Thorn partners across the technology companies and government organizations to combat predatory behavior, rescue victims, and protect vulnerable children.
Thorn has to sift through a massive amount of images daily. Images are processed using facial recognition, then classified and de-duplicated, and ultimately matched against millions of open web images. If an image match can be found faster, victims of trafficking and sexual exploitation can be helped faster.
With SingleStore, Thorn is able to accelerate their image recognition workflow. New vectors representing a face can be inserted and queried in real-time. This allows analyst to find image matches faster and improve law enforcement response times.
For more detail on our work with Thorn, see our recent Thorn case study. You can also watch our recorded keynote and download the slides below. In addition, we have a Q&A blog post from the engineers behind the application: http://blog.memsql.com/machine-learning-image-recognition-engineering-perspective/
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Gartner Magic Quadrant for Data Management Solutions for Analytics
The data warehouse as we know it has changed.
Growth in data size and complexity, migration to the cloud, and the rise of real-time use cases are forces pushing enterprise organizations to expect more from their data warehouse.
Evidence of this trend can be found in the latest Gartner Magic Quadrant Report, which has dropped data warehouse from its title and graduated to Data Management Solutions for Analytics. This change has resulted in an expansion of types of vendors covered, while also making the inclusion criteria significantly more challenging.
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How Manage Accelerated Data Freshness by 10x
Success in the mobile advertising industry is achieved by delivering contextual ads in the moment. The faster and more personalized a display ad, the better. Any delay in ad delivery means lost bids, revenue, and ultimately, customers.
Manage, a technology company specializing in programmatic mobile marketing and advertising, helps drive mobile application adoption for companies like Uber, Wish, and Amazon. In a single day, Manage generates more than a terabyte of data and processes more than 30 billion bid requests. Manage analyzes this data to know which impressions to buy on behalf of advertisers and uses machine learning models to predict the probability of clicks, app installs, and purchases.
Managing Data at Scale
At the start, Manage used MySQL to power their underlying statistics pipeline, but quickly ran into scaling issues as data volume grew. Manage then turned to Hadoop coupled with Apache Hive and Kafka for data management, analysis, and real-time data feeds. However, even with this optimized data architecture, Manage found that Hive was slow and caused hours of delay in data pipelines.
To meet customer expectations, Manage needed a solution that could deliver fresh data for reporting, while concurrently allowing their analytics team to run ad hoc queries. Kai Sung, Manage CTO and co-founder began the search for a faster database platform, and found SingleStore. The Manage team quickly started prototyping on SingleStore, and was in production within a few months.
Streaming Log Data from Apache Kafka
Manage uses SingleStore Streamliner, an Apache Spark solution, to first stream log data from Apache Kafka, then store it in the SingleStore columnstore for further processing. As new data arrives, the pipeline de-duplicates data and aggregates it into various summary tables within SingleStore. This data is then made available to an external reporting dashboard and reporting API. With this architecture, manage has a highly scalable, real-time data pipeline that ingests data and summarizes data as fast as it is produced.
10x Faster Data
After implementing SingleStore, Manage was able to reduce the delay in data freshness from two hours down to 10 to 15 minutes. With SingleStore, the Manage team now has the ability to run analytics much faster and can react to marketplace changes in the moment.
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Case Studies
How Kellogg Reduced 24-Hour ETL to Minutes and Boosted BI Speed by 20x
Background
About Kellogg
Kellogg Company is the world’s leading cereal company, second largest producer of cookies, crackers, and savory snacks, and a leading North American frozen foods company. With 2015 sales of \$13.5 billion, Kellogg produces more than 1,600 foods across 21 countries and markets its many brands in 180 countries.
Driving Revenue with Customer Logistics Data
Kellogg relies on customer logistics data to make informed decisions and improve efficiencies around shopping experiences. Accuracy and speed of such data is directly tied to the profitability of Kellogg’s business.
Leveraging In-Memory for Faster Access to Data
Making data readily available to business users is top-of-mind for Kellogg, which is why the company sought an in-memory solution to improve data latency and concurrency.
Starting with an initiative to speed access to customer logistics data, Kellogg turned to SingleStore to make its 24-hour ETL process faster. In an interview at Strata+Hadoop World, JR Cahill, Principal Architect for Global Analytics at Kellogg said:
“We wanted to see how we could transform processes to make ourselves more efficient and start looking at things more intraday rather than weekly to make faster decisions.”
Results
Reducing Latency from 24-Hours to Minutes
JR and team scaled up their SingleStore instance in AWS and within two weeks reduced the ETL process to an average of 43 minutes. On top of that, the team added three years of archiving into SingleStore, a feat not possible with their previous system, while maintaining an average ETL of 43 minutes.
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Product
Getting to Exactly-Once Semantics with Apache Kafka and SingleStore Pipelines (Webcast On-Demand)
The urgency for IT leaders to bring real-time analytics to their organizations is stronger than ever. For these organizations, the ability to start with fresh data and combine streaming, transactional, and analytical workloads in a single system can revolutionize their operations.
When moving from batch to real time, data architects should carefully consider what type of streaming semantics will optimize their workload. The table below highlights the nuances among different types of streaming semantics.
Understanding Streaming Semantics
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Data Intensity
The Path to Predictive Analytics and Machine Learning – Free O’REILLY Book
Organizations once waited hours, days, or even weeks to get a handle on their data. In an earlier era, that sufficed. But with today’s endless stream of zeros and ones, data must be usable right away. It’s the crux of decision making for enterprises competing in the modern era.
Recognizing cross-industry interest in massive data ingest and analytics, we teamed up with O’Reilly Media on a new book: The Path to Predictive Analytics and Machine Learning. In this book, we share the latest step in the real-time analytics journey: predictive analytics, and a playbook for building applications that take advantage of machine learning.
Free Download Here: The Path to Predictive Analytics and Machine Learning
What’s Inside?
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Data Intensity
Real-Time Analytics with Kafka and SingleStore
Connected devices, IoT, and on-demand user expectations push enterprises to deliver instant answers at scale. Applications that anticipate customer needs and fulfill expectations for fast, personalized services win the attention of consumers. Perceptive companies have taken note of these trends and are turning to memory-optimized technologies like Apache Kafka and SingleStore to power real-time analytics.
High Speed Ingest
Building real-time systems begins with capturing data at its source and using a high-throughput messaging system like Kafka. Taking advantage of a distributed architecture, Kafka is built to scale producers and consumers by simply adding servers to a given cluster. This effective use of memory, combined with commit log on disk, provides ideal performance for real-time pipelines and durability in the event of server failure. From there, data can be transformed and persisted to a database like SingleStore.
Fast, Performant Data Storage
SingleStore persists data from real-time streams coming from Kafka. By combining transactions and analytics in a memory-optimized system, data is rapidly ingested from Kafka, then persisted to SingleStore. Users can then build applications on top of SingleStore also supplies the application with the most recent data available.
We teamed up with the folks at Confluent, the creators of Apache Kafka, to share best practices for architecting real-time systems at our latest meetup. The video recording and slides from that session are now available below.
Meetup Video Recording: Real-Time Analytics with Confluent and SingleStore
Watch now to:
See a live demo of our new showcase application for modeling predictive analytics for global supply chain managementLearn how to architect systems for IoT streaming data ingestion and real-time analyticsLearn how to combine Kafka, Spark, and SingleStore for monitoring and optimizing global supply chain processes with real-time analytics
Video
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Case Studies
Top 5 SingleStore Showcase Applications and Product Demonstrations
We live in an age of digital realities. Most “goods” on the market are not physical entities, but more often software. However, consumers still need a tangible way to evaluate these digital products and services they are purchasing.
Enter the product demonstration – the best way to showcase the value of software in a short time.
At SingleStore, we pride ourselves on showcasing relatable product demonstrations from the main stage at Strata+Hadoop World to our locally hosted meetups.
Here’s a look at our top five showcase applications over the past year:
PowerStream IoT Application
PowerStream is an Internet of Things (IoT) application that displays visualizations and alerts from approximately 20,000 wind farms and 200,000 individual wind turbines. Temperature and vibration input from millions of sensors on turbines is ingested through Apache Kafka and inserted into SingleStore for real-time data exploration. Using this data and a machine learning algorithm, PowerStream predicts and visualizes the health of each turbine up to the last second.
Dataset
2 million sensors across 197,000 global wind turbines.
Results
Less than $20k annual cost. Runs on seven c4.2xlarge AWS instances at approximately $0.311 per instance/hour.
Processing more than 1 million transactions per second.
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Data and Analytics Predictions Through 2020
Everyone loves predictions. Especially, when said predictions are speculating on the future of technology adoption – where a slight change in user behavior could result in the next multi-billion dollar industry.
Gartner Analysts, Douglas Laney and Ankush Jain, recently published a research report with Gartner’s top 100 data and analytics predictions relevant to CIOs, CDOs, and analytics leaders. This research stems from hundreds of interactions with key stakeholders at data-driven organizations, and includes predictions on topics ranging from advanced analytics and data science to changes in business functions and industries.
For a limited time, SingleStore is hosting full access to this research. See all 100 predictions here ⇒
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The Path to Real-Time with SingleStoreDB Self-Managed 5 at Strata+Hadoop San Jose
At Strata+Hadoop World, we engaged with the brightest minds in data management, machine learning, and analytics. CEO and co-founder, Eric Frenkiel announced the release of SingleStoreDB Self-Managed 5 and showcased a path for real-time processing to the Strata audience during the day one keynote. Later, he shared the stage with Kellogg in a tutorial session on predictive analytics.
SingleStoreDB Self-Managed 5 Now Generally Available
Eric announced the general availability release of SingleStoreDB Self-Managed 5, delivering breakthrough performance on database, data warehouse, and streaming workloads.
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Complimentary Report: Gartner Magic Quadrant for Data Warehouse Solutions
Gartner has named SingleStore a Visionary in the Magic Quadrant for Data Warehouse and Management Solutions for Analytics!
This marks our debut in Gartner’s report for data warehouse solutions and our second showing on a Gartner Magic Quadrant report in the last six months.
The report compares top Data Warehouse solutions, as well as highlights key strengths that distinguish SingleStore from legacy vendors.
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Product
The Lambda Architecture Simplified (Guide)
Modern businesses need to support an increasing variety of data workloads and uses cases that require both fault-tolerance and scalability. This has led to widespread adoption of the Lambda Architecture.
Lambda is designed to model everything that happens in a complex computing system as an ordered, immutable log of events. Processing the data (for example, totaling the number of web visitors or transactions) is completed as a series of transformations that output to new tables or streams.
SingleStore combines database and data warehouse workloads, enabling both transactional processing and analytics. Gartner refers to this as HTAP or hybrid transaction/analytical processing. SingleStore often fulfills the speed layer of the Lambda architecture, providing in-memory performance to ingest and process streaming data. In our experience with Lambda implementations, we find that most organizations get hung up on details of the Lambda Architecture, introducing unnecessary technologies and workarounds to fit within the model. It doesn’t have to be this way.
Free Guide: The Lambda Architecture Simplified
To make sense of the Lambda Architecture and get you on track to a successful implementation, we are launching a new guide: The Lambda Architecture Simplified. This guide demystifies complexity surrounding the Lambda Architecture, and will reframe the way you think about the model by providing simplified data frameworks and real world use cases.
You’ll learn
What defines the Lambda Architecture, broken down by each layerHow to simplify the Lambda Architecture by consolidating the speed layer and batch layer into one systemHow to implement a scalable Lambda Architecture that accommodates streaming and immutable dataHow companies like Comcast and Tapjoy use Lambda Architectures in production
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Data Intensity
Characteristics of a Modern Database
Many legacy database systems are not equipped for modern applications. Near ubiquitous connectivity drives high-velocity, high-volume data workloads – think smartphones, connected devices, sensors – and a unique set of data management requirements. As the number of connected applications grows, businesses turn to in-memory solutions built to ingest and serve data simultaneously.
Bonus Material: Free O’Reilly Ebook – learn how to build real-time data pipelines with modern database architectures
To support such workloads successfully, database systems must have the following characteristics:
Modern Database Characteristics
Ingest and Process Data in Real Time\
Historically, the lag time between ingesting data and understanding that data has been hours to days. Now, companies require data access and exploration in real time to meet consumer expectations.
Subsecond Response Times\
As organizations supply access to fresh data, demand for access rises from hundreds to thousands of analysts. Serving this workload requires memory-optimized systems that process transactions and analytics concurrently.
Anomaly Detection as Events Occur\
Reaction time to an irregular event often correlates with a business’s financial health. The ability to detect an anomaly as it happens helps companies avoid massive losses and capitalize on opportunities.
Generate Reports Over Changing Datasets\
Today, companies expect analytics to run on changing datasets, where results are accurate to the last transaction. This real-time query capability has become a base requirement for modern workloads.
Real-Time Use Cases
Today, companies are using in-memory solutions to meet these requirements. Here are a few examples:
Pinterest: Real-Time Analytics\
Pinterest built a real-time data pipeline to ingest data into SingleStore using Spark Streaming. In this workflow, every Repin is filtered and enriched by adding geolocation and Repin category information. Enriched data is persisted to SingleStore and made available for query serving. This helps Pinterest build a better recommendation engine for showing Repins and enables their analysts to use familiar a SQL interface to explore real-time data and derive insights.
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![Building Real-Time Data Pipelines through In-Memory Architectures [Webcast]](https://images.contentstack.io/v3/assets/bltac01ee6daa3a1e14/blt8c64544168c8b0c1/63ea4b699c6cb020b6883ac4/featured_real-time-data-pipelines-webcast.jpg?width=278)
Data Intensity
Building Real-Time Data Pipelines through In-Memory Architectures [Webcast]
In the era of universal connectivity, the faster you can move data from point A to B the better. Equipping your organization with the ability to make frequent decisions in an instant offers information and intelligence advantages, such as staying one step ahead of the competition. This is especially important when incoming data is arriving at a relentless pace, in high volume, and from a variety of devices.
As our customers tap into new sources of data or modify to existing data pipelines, we are often asked questions like: What technologies should we consider? Where can we reduce data latency? How can we simplify data architectures?
To eliminate the guesswork, we teamed up with Ben Lorica, Chief Data Scientist at O’Reilly Media to host a webcast centered around building real-time data pipelines.
Watch the recorded webcast to learn:
Ideal technology stacks for building real-time data pipelinesHow to simplify Lambda architecturesHow to use memory-optimized technologies like Kafka, Spark, and in-memory databases to build real-time data pipelinesUse cases for real-time workloads, and the value they offerExamples of data architectures used by companies like Pinterest and Comcast
Webcast Recording
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Find Your IoT Use Case
As enterprises invest billions of dollars in solutions for the Internet of Things (IoT), business leaders seek compelling IoT use cases that tap into new sources of revenue and maximize operational efficiency. At the same time, advancements in data architectures and in-memory computing continue to fuel the IoT fire, enabling organizations to affordably operate at the speed and scale of IoT.
In a recent webcast, Matt Aslett, Research Director at 451 Research, shared use cases across six markets where IoT will have a clear impact:
Watch the IoT and Multi-model Data Infrastructure Webcast Recording
Industrial Automation
Seen as the ‘roots of IoT’, organizations in the industrial automation sector are improving performance and reducing downtimes by adding automation through sensors and making data available online.
Utilities
When people think about IoT, household utilities like thermostats and smoke alarms often come to mind. Smart versions of these devices not only benefit consumers, but also help utility providers operate efficiently, resulting in savings for all parties.
Retail
Bringing radio-frequency identification (RFID) online allows retailers to implement just-in-time (JIT) stock-keeping to cut inventory costs. Additionally, retailers can provide better shopping experiences in the form of mobilized point-of-sale systems and contextually relevant offers.
Healthcare
Connected health equipment allows for real-time health monitoring and alerts that offer improved patient treatment, diagnosis, and awareness.
Transportation and Logistics
IoT is improving efficiency in transportation and logistics markets by providing benefits like just-in-time manufacturing and delivery, as well as improved customer service.
Automotive
The automobile industry is improving efficiencies through predictive maintenance and internet enabled fault diagnostics. Another interesting use case comes from capturing driving activity, as insurance companies can better predict driver risk and offer discounts (or premiums) based on data from the road.
Finding the Internet of Your Things
To take advantage of IoT, Matt notes that it is paramount to identify what top priorities are in your specific case by asking the following questions:
Are there ‘things’ within your organization that would benefit from greater connectivity?
Can better use be made of the ‘things’ that are already network-ready and the data they create?
Are there ‘things’ outside the organization that would benefit from greater connectivity?
Is there a way to reap value from your customers, partners, or suppliers’ smart devices that would be mutually beneficial?
If you answered ‘yes’ to any of these questions, there is a good chance your organization can improve efficiency with an IoT solution. To get started, watch the recording of the IoT and multi-model data infrastructure webcast and view the slides here:
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Data Intensity
Making Faster Decisions with Real-Time Data Pipelines
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Join SingleStore in Boston for Big Data Innovation Summit
The Big Data Innovation Summit kicks off in Boston today, uniting some of the biggest data-driven brands, like Nike, Uber, and Airbnb. The conference is an opportunity for industry leaders to share diverse big data initiatives and learn how to approach prominent data challenges.
We are exhibiting at booth #23 and will showcase several demos: MemCity, Supercar, and Real-time Analytics for Pinterest. On top of that, we will have games and giveaways at the booth, as well as complimentary download of the latest Forrester Wave report on in-memory database platforms. More on what to expect:
Demos
MemCity – a simulation that measures and maps the energy consumption across 1.4 million households in a futuristic city, approximately the size of Chicago. MemCity is made possible through a real-time data pipeline built from Apache Kafka, Apache Spark, and SingleStore.
Supercar – showcases real-time geospatial intelligence features of SingleStore. The demo is built off a dataset containing the details of 170 million real world taxi rides. Supercar allows users to select a variety of queries to run on the ride data, such as the average trip length during a determined set of time. The real-world application of this is business or traffic analysts can monitor activity across hundreds of thousands of vehicles, and identify critical metrics, like how many rides were served and average trip time.
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Gearing Up for Gartner Catalyst in San Diego
Gartner Catalyst Conference kicks off next week, Aug 10-13 in San Diego, and we are thrilled to speak and exhibit at the event. Stop by the SingleStore booth #518 to see our latest demos: MemCity, Supercar, and Pinterest. SingleStore CEO, Eric Frenkiel, and the SingleStore product team will be available at the booth to answer any questions. Book a 1:1 ahead of time with a SingleStore expert here. On top of that, we have a speaking session, happy hour, games and giveaways planned.
Here’s what you can expect:
Speaking Session: Real-Time Data Pipelines with Kafka, Spark, and Operational Databases
12:45 – 1:05 PM Harbor Ballroom – Tuesday, August 11.
What happens when trillions of sensors go online? By 2020, this could be a reality and real-time mobile applications will become integral to capturing, processing, analyzing and serving massive amounts of data from these sensors to millions of users. In this session, Eric Frenkiel, CEO and Co-Founder of SingleStore, will share how-to recipes for building your own real-time data pipeline and applications today with Apache Kafka, memory-optimized Apache Spark, and SingleStore.
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Join SingleStore at the Data Science Summit in San Francisco
We are excited to exhibit at the Data Science Summit on Monday, July 20, in San Francisco. Stop by the SingleStore booth to learn about our MemCity demo, pickup a cool t-shirt, and play our reaction test game to win an Estes ProtoX Mini Drone.
About the Data Science Summit
The Data Science Summit is a non-profit event that connects researchers and data scientists from academia and industry to discuss the art of data science, machine learning, and predictive applications.
What We Have in Store for the Event
Visit the SingleStore booth to learn about:
Our latest demo, MemCity, that leverages Kafka, Spark, and SingleStore to process and analyze data from various energy devices found in homes, all measured in real time.How in-memory computing can combat latencies in the enterprise, such as batch loading and query execution latency.How SingleStore enables data analyst to get real-time insights using SQL.SingleStore Community Edition – a free downloadable version of SingleStore that comes without limitations on capacity, cluster size, or time.
Recommended Sessions
How Comcast uses Data Science to Improve the Customer Experience
Monday, July 20, 10:50am – Salon 9
Comcast Labs manager, Dr. Jan Neumann, will discuss how Comcast improves the visible parts of the user experience by powering the personalized content discovery algorithms and voice interface on the X1 set top boxes. Bonus: Learn how the Comcast VIPER team is using SingleStore for real-time stream processing.
What’s New in the Berkeley Data Analytics Stack
Monday, July 20, 1:20pm – Salon 9
In this talk, Prof. Mike Franklin of the Berkeley AMPLab will give a quick overview of BDAS (pronounced “badass”) and then describe several newer BDAS components including: the KeystoneML machine learning pipeline framework, the Velox model serving layer, and the SampleClean/AMPCrowd components for human-in-the-loop data cleaning and machine learning.
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Join SingleStore at the Inaugural In-Memory Computing Summit
The inaugural In-Memory Computing Summit begins next week, June 29-30, and we are thrilled to be a part of it. From speaking sessions on Spark and customer use cases, to games and giveaways, you will not want to miss the action. Visit us at booth #4 to pick up our brand new t-shirt, and learn how in-memory computing can bring peak performance to new or existing applications.
SingleStore Speaking Sessions
From Spark to Ignition: Fueling Your Business on Real-Time Analytics
Monday, June 29 at 10:40am – Eric Frenkiel, SingleStore CEO and Co-Founder
Real-time is the next phase of big data. For the modern enterprise, processing and analyzing large volumes of data quickly is integral to success. SingleStore and Spark share design philosophies like memory-optimized data processing and scale-out on commodity hardware that enable enterprises to build real-time time data pipelines with operational data always online. This session shares hands-on ‘how-to’ recipes to build workflows around Apache Spark, with detailed production examples covered.
A Hitchhiker’s Guide to the Startup Data Science Platform
Monday, June 29 at 4:40pm – David Abercrombie, Principal Data Analytics Engineer at Tapjoy
Join David Abercrombie for a session chronicling the growth of the Tapjoy data science team as a lens for examining the infrastructure and technology most critical to their success. This includes implementations and integrations of Hadoop, Spark, NoSQL and SingleStore, which enable Tapjoy to turn sophisticated algorithms into serviceable, data-driven products.
Resources to Gear Up for the Event
Gartner Market Guide for In-Memory DBMS
This complimentary guide from Gartner provides a comprehensive overview of the in-memory database landscape. Download it to learn about three major use cases for in-memory databases and Gartner’s recommendations for evaluation and effective use.
Download the Guide
The Modern Database Landscape
Download this white paper to learn how in-memory computing enables transactions and analytics to be processed in a single system and how to leverage converged processing to save time and cut costs, while providing real-time insights that facilitate data-driven decision-making.
Download the White Paper
Games and Giveaways
Drop by the SingleStore booth #4 to get your free, super-soft t-shirt and play our reaction test game for a chance to win an Estes ProtoX Mini Drone.
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Join SingleStore at Spark Summit
We’re excited to be at Spark Summit next week in our hometown of San Francisco. If you’re attending, stop by booth K6 for games and giveaways, and checkout our latest demo that showcases how organizations are using SingleStore and Spark for real-time analytics.
Meet with us at Spark Summit
Schedule an in-person meeting or demo at the event.
Reserve a Time →
SingleStore and Spark Highlights
Over the past year, we’ve been working closely with our customers and the Spark community to build real-time applications powered by Spark and SingleStore. Highlights include:
Real-Time Analytics at Pinterest with Spark and SingleStore
Learn how Pinterest built a real-time data pipeline with SingleStore and Spark Streaming to achieve higher performance event logging, reliable log transport, and faster query execution on real-time data.
Read the full post on the Pinterest Engineering Blog
SingleStore Spark Connector
The SingleStore Spark Connector provides everything you need to start using Spark and SingleStore together. It comes with a number of optimizations, such as reading data out of SingleStore in parallel and making sure that Spark colocates data in its cluster.
Download now on GitHub
Building Real-Time Platforms with Apache Spark
Watch our session from Strata+Hadoop World to learn how hybrid transactional and analytical data processing capabilities, integrated with Apache Spark, enable businesses to build real-time platforms for applications.
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SingleStore at Gartner Business Analytics and Intelligence Summit
We are thrilled to be in Las Vegas this week for the Gartner Business Analytics and Intelligence Summit. We will be at booth #119 and we have a ton in store for the event, including games and giveaways, happy hour for attendees, and a featured session from SingleStore CEO, Eric Frenkiel.
We will also be showcasing our new geospatial capabilities, and a demo of how Pinterest is using SingleStore and Spark for real-time analytics.
Free Gartner Report: Market Guide for In-Memory DBMS
See the latest developments and use cases for in-memory databases.
Download the Report Here →
From the report…
“The growing number of high performance, response-time critical and low-latency use cases (such as real-time repricing, power grid rerouting, logistics optimization), which are fast becoming vital for better business insight, require faster database querying, concurrency of access and faster transactional and analytical processing. IMDBMSs provide a potential solution to all these challenging use cases, thereby accelerating its adoption.”
Don’t Miss the SingleStore Featured Session
From Spark to Ignition: Fueling Your Business on Real-Time Analytics
SingleStore CEO and Founder, Eric Frenkiel, will discuss how moving from batch-oriented data silos to real-time pipelines means replacing batch processes with online datasets that can be modified and queried concurrently. This session will cover use cases and customer deployments of Hybrid Transaction/Analytic Processing (HTAP) using SingleStore and Spark.
Session Details
Speaker: Eric Frenkiel, SingleStore CEO and FounderData and Time: 12:30pm–12:50pm Monday, 3/30/2015Location: Theater A, Forum Ballroom
Join SingleStore on Monday Night for Happy Hour
We will be hosting a happy hour at Carmine’s in The Form Shops at Caesars on Monday night at 8:00PM. ALTER TABLE TINIs and heavy hors d’oeuvres will be served. Stop by and meet with SingleStore CEO, Eric Frenkiel and CMO, Gary Orenstein. More details here.
Suggested Sessions
We have handpicked a few sessions that you don’t want to miss.
Do We Still Need a Data Warehouse?
Speaker: Donald Feinberg VP Distinguished Analyst
30 March 2015 2:00 PM to 2:45 PM
For more than a decade, the data warehouse has been the architectural foundation of most BI and analytic activity. However, various trends (in-memory, Hadoop, big data and the Internet of Things) have compelled many to ask whether the data warehouse is still needed. This session provides guidance on how to craft a more modern strategy for data warehousing.
Will Hadoop Jump the Spark?
Speaker: Merv Adrian Research VP
31 March 2015 2:00 PM to 2:45 PM
The Hadoop stack continues its dramatic transformation. The emergence of Apache Spark, suitable for many parts of your analytic portfolio, will rewrite the rules, but its readiness and maturity are in question.
The DBMS Dilemma: Choosing the Right DBMS For The Digital Business
Speaker: Donald Feinberg VP Distinguished Analyst
31 March 2015 2:00 PM to 2:45 PM
As your organization moves into the digital business era, the DBMS needs to support not only new information types but also the new transactions and analytics required for the future. The DBMS as we know it is changing. This session will explore the new information types, new transaction types and the technology necessary to support this.
Games and Giveaways
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Trending
Video: The State of In-Memory and Apache Spark
Strata+Hadoop World was full of activity for SingleStore. Our keynote explained why real-time is the next phase for big data. We showcased a live application with Pinterest where they combine Spark and SingleStore to ingest and analyze real-time data. And we gave away dozens of prizes to Strata+Hadoop attendees who proved their latency crushing skills in our Query Kong game.
During the event, Mike Hendrickson of O’Reilly Media sat down with SingleStore CEO Eric Frenkiel to discuss:
The state of in-memory computing and where it will be in a yearWhat Spark brings to in-memory computingIndustries and use cases that are best suited for Spark
Get The SingleStore Spark Connector Guide
The 79 page guide covers how to design, build, and deploy Spark applications using the SingleStore Spark Connector. Inside, you will find code samples to help you get started and performance recommendations for your production-ready Apache Spark and SingleStore implementations.
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Four Ways Your DBMS is Holding You Back – And One Simple Fix
Our data is changing faster than our data centers, making it harder and harder to keep up with the influx of incoming information, let alone make use of it. IT teams still tolerate overnight batch processing. The cost of scaling legacy solutions remains cost prohibitive. And many promised solutions force a complete departure from the past.
If this sounds familiar, you are not alone. Far too many innovative companies struggle to build applications for the future on infrastructure of the past. It’s time for a new approach.
In their report, “Hybrid Transaction/Analytical Processing Will Foster Opportunities for Dramatic Business Innovation,” Gartner identifies four major drawbacks of traditional database management systems, and how a new approach of hybrid transactional and analytical processing can solve these issues.
A Brief Overview of HTAP
Hybrid transactional/analytical processing (HTAP) merges two formerly distinct categories of data management: operational databases that processed transactions, and data warehouses that processed analytics. Combining these functions into a single system inherently eliminates many challenges faced by database administrators today.
How HTAP Remedies the Four Drawbacks of Traditional Systems
ETL
In HTAP, data doesn’t need to move from operational databases to separated data warehouses/data marts to support analytics. Rather, data is processed in a single system of record, effectively eliminating the need to extract, transform, and load (ETL) data. This benefit provides much welcomed relief to data analysts and administrators, as ETL often takes hours (sometimes days) to complete.
Analytic Latency
In HTAP, transactional data of applications is readily available for analytics when created. As a result, HTAP provides an accurate representation of data as it’s being created, allowing businesses to power applications and monitor infrastructure in real-time.
Synchronization
In HTAP, drill-down from analytic aggregates always points to the “fresh” HTAP application data. Contrast that with a traditional architecture, where analytical and transactional data is stored in silos, and building a system to synchronize data stores quickly and accurately is cumbersome. On top of that, it’s likely that the “analytics copy” of data will be stale and provide a false representation of data.
Copies of Data
In HTAP, the need to create multiple copies of the same data is eliminated (or at least reduced). Compared to a traditional architectures, where copies of data must managed and monitored for consistency, HTAP reduces inaccuracies and timing differences associated with the duplication of data. The result is a simplified system architecture that mitigates the complexity of managing data and hardware costs.
Why HTAP and Why Now?
One of the reasons we segmented workloads in the past was to optimize for specific hardware, especially disk drives. In order to meet performance needs, systems designed for transactions were best optimized one way, and systems designed for queries another. Merging systems on top of the same set of disk drives would have been impossible from a performance perspective.
With the advent of low cost, memory-rich servers, in your data center or in the cloud, new in-memory databases can transcend prior restrictions and foster simplified deployments for existing use cases while simultaneously opening doors to new data centric applications.
Want to learn more about in-memory databases and opportunities with HTAP? – Take a look at the recent Gartner report here.
If you’re interested in test driving an in-memory database that offers the full benefits of HTAP, give SingleStore a try for 30 days, or give us a ring.
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Market Guide for In-Memory DBMS
From the inception of SingleStore, we’ve seen the ability to merge transactions and analytics into a single database system as a core function for any data centric organization. Gartner’s recent report, “Market Guide for In-Memory DBMS” mirrors that belief, and is chock full of key findings and recommendations for businesses looking to take advantage of in-memory computing.
Skip this article and download a complimentary copy of Gartner’s Market Guide for In-Memory DBMS
In the report, Gartner found that “rapid technological advances in in-memory computing (IMC) have led to the emergence of hybrid transactional/analytical processing (HTAP) architectures that allow concurrent analytical and transactional processing on the same IMDBMS or data store.”
HTAP Solves for Real-Time Data Processing
HTAP promises to open a green field of opportunities for businesses that are not possible with legacy database management systems. Gartner highlights that with HTAP, “large volumes of complex business data can be analyzed in real time using intuitive data exploration and analysis without the latency of offloading the data to a data mart or data warehouse. This will allow business users to make more informed operational and tactical decisions.”
HTAP Use Cases
We are in the early days of HTAP, and it is not always clear how it can be applied in the real world. As a rule of thumb, any organization that handles large volumes of data will benefit from HTAP. To provide a bit more context, we’ve compiled the following applications of HTAP in use today.
Application Monitoring
When millions of users reach mobile or web-based applications simultaneously, it’s critical that systems run without any hiccups. HTAP allows teams of system administrators and analysts to monitor the health of applications in real-time to spot anomalies and save on costs incurred from poor performance.
Internet of Things
Applications built for the internet of things (IoT) run on huge amounts of sensor data. HTAP easily processes IoT scale data workloads, as it is designed to handle extreme data ingestion while concurrently making analytics available in real-time.
Real-Time Bidding
Ad Tech companies struggle to implement complex real-time bidding features due of the sheer volume of data processing required. HTAP delivers the processing power that’s necessary to serve display, social, mobile and video advertising at scale.
Market Conditions
Financial organizations must be able to respond to market volatility in an instant. Any delay is money out of their pocket. HTAP makes it possible for financial institutions to respond to fluctuating market conditions as they happen.
In each of these use cases, the ability to react to large data sets in a short amount of time provides incredible value and, with HTAP, is entirely possible.
Finding the Right In-Memory DBMS
Before diving into a proof of concept, we highly suggest reading Gartner’s “Market Guide for In-Memory DBMS.” By giving it a quick read, you’ll come away with a better understanding of the in-memory computing landscape, new business opportunities, applicable use cases for your organization, and an action plan for getting started.
For a limited time, we’re offering a complimentary download of the report. Download it now to learn:
Why In-memory computing is growing in popularity and adoptionHow IMDBMSs are categorized and the three major use cases they supportNew business opportunities emerging from hybrid transactional and analytical processing (HTAP)How to jump ahead of the competition with recommendations for effective use of IMDBMS
Get a better understanding of the in-memory computing landscape. Download the Gartner Market Guide here. – Click to Tweet
Required Disclaimer: Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
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Predictions for Apache Spark, IoT, and In-Memory Computing – Video Interview with Mike Hendrickson of O’Reilly Media
While at Strata+Hadoop World, SingleStore CEO, Eric Frenkiel, sat down with Mike Hendrickson of O’Reilly Media for a conversation about the evolution of the database ecosystem, and how Hadoop and in-memory computing fit into the picture.
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