An Engineering Approach to Database EvaluationsSingleStore
This talk will go over a methodical approach for making a decision, dig into interesting tradeoffs, and give tips about what things to look for under the hood and how to evaluate the tech behind the database.
"Building Real-Time Data Pipelines with Kafka and MemSQL" by Rick Negrin, Director of Product Management at MemSQL for Orange County Roadshow March 17, 2017.
O'Reilly Media Webcast: Building Real-Time Data PipelinesSingleStore
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 our data architecture?
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.
Converging Database Transactions and Analytics SingleStore
delivered at the Gartner Data and Analytics 2018 show in Texas. This presentation discusses real-time applications and their impact on existing data infrastructures
Winning the On-Demand Economy with Spark and Predictive AnalyticsSingleStore
Today’s on-demand economy drives companies to provide fast load times, personalization, and instantaneous service for hungry end-users across all types of applications. Yet most still use dated, legacy systems to process and analyze data. In this session, Ankur Goyal, VP of Engineering at MemSQL will showcase implementing a one-click Lambda Architecture with Apache Spark, Apache Kafka and an operational database, resulting in lightning fast analytics on large, changing datasets.
Bringing olap fully online analyze changing datasets in mem sql and spark wi...SingleStore
As the world moves from batch to online data processing, real-time data pipelines will supercede siloed data warehouse and transaction processing systems as core infrastructure.
While many analytics solutions tout query execution speed, this is only half of the equation.
For real time workloads, stale data renders query speed irrelevant when results and insights are out of date.
Beyond just “online queries,” real-time enterprises need “online datasets” that continuously update and make data accessible across the organization.
This session will cover approaches to building real-time pipelines with MemSQL, Hadoop, and Spark. Topics will include:
Key industry trends and the move to real-time data pipelines
How MemSQL customer Novus built the premier financial portfolio management platform using MemSQL as a real-time data store and query engine.
Operationalizing Spark for Advanced Analytics
Demonstration of how Pinterest is using the MemSQL Spark Connector to derive real-time insights on interesting and meaningful user activity with MemSQL and Spark.
Introduction to the MemSQL Spark Connector
Strategies for integrating Spark and Hadoop with real-time systems for transaction processing and operational analytics.
Presenters include MemSQL CEO Eric Frenkiel, Novus CTO Robert Stepeck, and Pinterest Software Engineer Yu Yang.
In a world of web portals and push notifications, users have developed demanding expectations for a real-time experience. Continuous updates, a responsive interface, and short loading times have become the norm. Most business analysts and data scientists, whose workflows remain bound by legacy tools and complex data pipelines, lack this fast, simple user experience.
From a business perspective, latency and complexity impede revenue by preventing access to the right data at the right time. Businesses that recognize the value of access to real-time data now have options to meet stringent objectives. They understand that serving “always up to date” data for analysis requires converging transactions and analytics in a real-time system. This session will highlight these architectures and customer achievements.
Tapjoy: Building a Real-Time Data Science Service for Mobile AdvertisingSingleStore
Robin Li, Director of Data Engineering and Yohan Chin, VP Data Science at Tapjoy share how to architect the best application experience for mobile users using technologies including Apache Kafka, Apache Spark, and MemSQL.
Speaker: Robin Li - Director of Data Engineering, Tapjoy and Yohan Chin - VP Data Science, Tapjoy
An Engineering Approach to Database EvaluationsSingleStore
This talk will go over a methodical approach for making a decision, dig into interesting tradeoffs, and give tips about what things to look for under the hood and how to evaluate the tech behind the database.
"Building Real-Time Data Pipelines with Kafka and MemSQL" by Rick Negrin, Director of Product Management at MemSQL for Orange County Roadshow March 17, 2017.
O'Reilly Media Webcast: Building Real-Time Data PipelinesSingleStore
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 our data architecture?
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.
Converging Database Transactions and Analytics SingleStore
delivered at the Gartner Data and Analytics 2018 show in Texas. This presentation discusses real-time applications and their impact on existing data infrastructures
Winning the On-Demand Economy with Spark and Predictive AnalyticsSingleStore
Today’s on-demand economy drives companies to provide fast load times, personalization, and instantaneous service for hungry end-users across all types of applications. Yet most still use dated, legacy systems to process and analyze data. In this session, Ankur Goyal, VP of Engineering at MemSQL will showcase implementing a one-click Lambda Architecture with Apache Spark, Apache Kafka and an operational database, resulting in lightning fast analytics on large, changing datasets.
Bringing olap fully online analyze changing datasets in mem sql and spark wi...SingleStore
As the world moves from batch to online data processing, real-time data pipelines will supercede siloed data warehouse and transaction processing systems as core infrastructure.
While many analytics solutions tout query execution speed, this is only half of the equation.
For real time workloads, stale data renders query speed irrelevant when results and insights are out of date.
Beyond just “online queries,” real-time enterprises need “online datasets” that continuously update and make data accessible across the organization.
This session will cover approaches to building real-time pipelines with MemSQL, Hadoop, and Spark. Topics will include:
Key industry trends and the move to real-time data pipelines
How MemSQL customer Novus built the premier financial portfolio management platform using MemSQL as a real-time data store and query engine.
Operationalizing Spark for Advanced Analytics
Demonstration of how Pinterest is using the MemSQL Spark Connector to derive real-time insights on interesting and meaningful user activity with MemSQL and Spark.
Introduction to the MemSQL Spark Connector
Strategies for integrating Spark and Hadoop with real-time systems for transaction processing and operational analytics.
Presenters include MemSQL CEO Eric Frenkiel, Novus CTO Robert Stepeck, and Pinterest Software Engineer Yu Yang.
In a world of web portals and push notifications, users have developed demanding expectations for a real-time experience. Continuous updates, a responsive interface, and short loading times have become the norm. Most business analysts and data scientists, whose workflows remain bound by legacy tools and complex data pipelines, lack this fast, simple user experience.
From a business perspective, latency and complexity impede revenue by preventing access to the right data at the right time. Businesses that recognize the value of access to real-time data now have options to meet stringent objectives. They understand that serving “always up to date” data for analysis requires converging transactions and analytics in a real-time system. This session will highlight these architectures and customer achievements.
Tapjoy: Building a Real-Time Data Science Service for Mobile AdvertisingSingleStore
Robin Li, Director of Data Engineering and Yohan Chin, VP Data Science at Tapjoy share how to architect the best application experience for mobile users using technologies including Apache Kafka, Apache Spark, and MemSQL.
Speaker: Robin Li - Director of Data Engineering, Tapjoy and Yohan Chin - VP Data Science, Tapjoy
Building the Ideal Stack for Machine LearningSingleStore
Machine Learning is not new, but its application across memory-optimized distributed systems has led to an explosion in both the number and capability of its uses. Pandora develops personalized content recommendations with machine learning algorithms, Tesla has produced the first widely distributed autonomous vehicle, and Amazon uses autonomous robots to move packages within its warehouses and even deliver packages. When coupled with real-time data, advanced analytics approaches like machine learning and deep learning create immediate business opportunities.
Machine learning has never been more accessible—if your data pipelines support real-time analysis. Attendees will learn tools and techniques for integrating machine learning models across industries and organizations. Steven Camiña, MemSQL Product Manager, will walk through critical technologies needed in your technology ecosystem, including Python, Apache Kafka, Apache Spark, and a real-time database.
Strata+Hadoop 2017 San Jose - The Rise of Real Time: Apache Kafka and the Str...confluent
The move to streaming architectures from batch processing is a revolution in how companies use data. But what is the state of the union for stream processing, and what gaps remain in the technology we have? How will this technology impact the architectures and applications of the future? Jay Kreps explores the future of Apache Kafka and the stream processing ecosystem.
Data Pipelines Made Simple with Apache Kafkaconfluent
Presentation by Ewen Cheslack-Postava, Engineer, Apache Kafka Committer, Confluent
In streaming workloads, often times data produced at the source is not useful down the pipeline or it requires some transformation to get it into usable shape. Similarly, where sensitive data is concerned, filtering of topics is helpful to ensure that the wrong data doesn't get to the wrong place.
The newest release of Apache Kafka now offers the ability to do transformations on individual messages, making is possible to implement finer grained transformations customized to your unique needs. In this session we’ll talk about the new single message transform capabilities, how to use them to implement things like data masking and advanced partitioning, and when you’ll need to use more complex tools like the Kafka Streams API instead.
If your heart cracks when you hear terrible stories about preschoolers having chemo when they should be having nothing but fun; if you think the teams of doctors and scientists working tirelessly to end childhood disease deserve the means to continue research and healing....
Streaming Analytics with Spark, Kafka, Cassandra and AkkaHelena Edelson
This talk will address how a new architecture is emerging for analytics, based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK). Popular architecture like Lambda separate layers of computation and delivery and require many technologies which have overlapping functionality. Some of this results in duplicated code, untyped processes, or high operational overhead, let alone the cost (i.e. ETL). I will discuss the problem domain and what is needed in terms of strategies, architecture and application design and code to begin leveraging simpler data flows. We will cover how the particular set of technologies addresses common requirements and how collaboratively they work together to enrich and reinforce each other.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...Databricks
At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure.
GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Dynamic DDL: Adding structure to streaming IoT data on the flyDataWorks Summit
At the end of day the only thing that data scientists want is one thing. They want tabular data for their analysis.
They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data
that is being streamed at them from IoT devices and apps and at the same time add structure to it so that data scientists
can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds).
Oh... and there are a bunch more data sources that you need to ingest and the current providers of data are changing their structure.
At GoPro, we have massive amounts of heterogeneous data being streamed at us from our consumer devices
and applications, and we have developed a concept of "dynamic DDL" to structure our streamed data on the fly using
Spark Streaming, Kafka, HBase, Hive, and S3. The idea is simple. Add structure (schema) to the data as soon as possible.
Allow the providers of the data to dictate the structure. And automatically create event-based and state-based tables (DDL)
for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
Tackling the challenge of designing a machine learning model and putting it into production is the key to getting value back – and the roadblock that stops many promising machine learning projects. After the data scientists have done their part, engineering robust production data pipelines has its own set of challenges. Syncsort software helps the data engineer every step of the way.
Building on the process of finding and matching duplicates to resolve entities, the next step is to set up a continuous streaming flow of data from data sources so that as the sources change, new data automatically gets pushed through the same transformation and cleansing data flow – into the arms of machine learning models.
Some of your sources may already be streaming, but the rest are sitting in transactional databases that change hundreds or thousands of times a day. The challenge is that you can’t affect performance of data sources that run key applications, so putting something like database triggers in place is not the best idea. Using Apache Kafka or similar technologies as the backbone to moving data around doesn’t solve the problem of needing to grab changes from the source pushing them into Kafka and consuming the data from Kafka to be processed. If something unexpected happens – like connectivity is lost on either the source or the target side, you don’t want to have to fix it or start over because the data is out of sync.
View this 15-minute webcast on-demand to learn how to tackle these challenges in large scale production implementations.
An AMIS Overview of Oracle database 12c (12.1)Marco Gralike
Presentation used by Lucas Jellema and Marco Gralike during the AMIS Oracle Database 12c Launch event on Monday the 15th of July 2013 (much thanks to Tom Kyte, Oracle, for being allowed to use some of his material)
M.
AMIS organiseerde op maandagavond 15 juli het seminar ‘Oracle database 12c revealed’. Deze avond bood AMIS Oracle professionals de eerste mogelijkheid om de vernieuwingen in Oracle database 12c in actie te zien! De AMIS specialisten die meer dan een jaar bèta testen hebben uitgevoerd lieten zien wat er nieuw is en hoe we dat de komende jaren gaan inzetten!
Deze presentatie is deze avond gegeven als een plenaire sessie!
This presentation looks at how to build an architecture for big and fast data. It reviews the Kappa & Lambda architectures and looks at the role Hazelcast Jet & IMDG can play in the Kappa architecture. It then proposes an evolution of the Kappa architecture to provide a transactional big data system.
Building a Pluggable Analytics Stack with Cassandra (Jim Peregord, Element Co...DataStax
Element Fleet has the largest benchmark database in our industry and we needed a robust and linearly scalable platform to turn this data into actionable insights for our customers. The platform needed to support advanced analytics, streaming data sets, and traditional business intelligence use cases.
In this presentation, we will discuss how we built a single, unified platform for both Advanced Analytics and traditional Business Intelligence using Cassandra on DSE. With Cassandra as our foundation, we are able to plug in the appropriate technology to meet varied use cases. The platform we’ve built supports real-time streaming (Spark Streaming/Kafka), batch and streaming analytics (PySpark, Spark Streaming), and traditional BI/data warehousing (C*/FiloDB). In this talk, we are going to explore the entire tech stack and the challenges we faced trying support the above use cases. We will specifically discuss how we ingest and analyze IoT (vehicle telematics data) in real-time and batch, combine data from multiple data sources into to single data model, and support standardized and ah-hoc reporting requirements.
About the Speaker
Jim Peregord Vice President - Analytics, Business Intelligence, Data Management, Element Corp.
Webinar: Enterprise Data Management in the Era of MongoDB and Data LakesMongoDB
With so much talk of how Big Data is revolutionizing the world and how a data lake with Hadoop and/or Spark will solve all your data problems, it is hard to tell what is hype, reality, or somewhere in-between.
In working with dozens of enterprises in varying stages of their enterprise data management (EDM) strategy, MongoDB enterprise architect, Matt Kalan, sees the same challenges and misunderstandings arise again and again.
In this session, he will explain common challenges in data management, what capabilities are necessary, and what the future state of architecture looks like. MongoDB is uniquely capable of filling common gaps in the data lake strategy.
This session also includes a live Q&A portion during which you are encouraged to ask questions of our team.
Apache Spark for RDBMS Practitioners: How I Learned to Stop Worrying and Lov...Databricks
This talk is about sharing experience and lessons learned on setting up and running the Apache Spark service inside the database group at CERN. It covers the many aspects of this change with examples taken from use cases and projects at the CERN Hadoop, Spark, streaming and database services. The talks is aimed at developers, DBAs, service managers and members of the Spark community who are using and/or investigating “Big Data” solutions deployed alongside relational database processing systems. The talk highlights key aspects of Apache Spark that have fuelled its rapid adoption for CERN use cases and for the data processing community at large, including the fact that it provides easy to use APIs that unify, under one large umbrella, many different types of data processing workloads from ETL, to SQL reporting to ML.
Spark can also easily integrate a large variety of data sources, from file-based formats to relational databases and more. Notably, Spark can easily scale up data pipelines and workloads from laptops to large clusters of commodity hardware or on the cloud. The talk also addresses some key points about the adoption process and learning curve around Apache Spark and the related “Big Data” tools for a community of developers and DBAs at CERN with a background in relational database operations.
Similar to Real-Time Analytics with Spark and MemSQL (20)
The database market is large and filled with many solutions. In this talk, Seth Luersen from MemSQL we will take a look at what is happening within AWS, the overall data landscape, and how customers can benefit from using MemSQL within the AWS ecosystem.
MemSQL 201: Advanced Tips and Tricks WebcastSingleStore
Topics discussed include differences between columnstore and rowstore engines, data ingestion, data sharding and query tuning, lastly memory and workload management.
Watch the replay at https://memsql.wistia.com/medias/4siccvlorm
Building a Fault Tolerant Distributed ArchitectureSingleStore
This talk will highlight some of the challenges to building a fault tolerant distributed architecture, and how MemSQL's architecture tackles these challenges.
Stream Processing with Pipelines and Stored ProceduresSingleStore
This talk will discuss an upcoming feature in MemSQL 6.5 showing how advanced stream processing use cases can be tackled with a combination of stored procedures (new in 6.0) and MemSQL's pipelines feature.
Learn how to leverage MPP technology and distributed data to deliver high volume transactional and analytical work loads which result in real time dashboards on rapidly changing data using standard SQL tools. Demonstrations will include the streaming of structured and JSON data from Kafka messages through a micro-batch ETL process into the MemSQL database where the data is then queried using standard SQL tools and visualized leveraging Tableau.
This session will focus on image recognition, the techniques available, and how to put those techniques into production. It will further explore algebraic operations on tensors, and how that can assist in large-scale, high-throughput, highly-parallel image recognition.
LIVE DEMO: Constructing and executing a real-time image recognition pipeline using Kafka and Spark.
Speaker: Neil Dahlke, MemSQL Senior Solutions Engineer
How Database Convergence Impacts the Coming Decades of Data ManagementSingleStore
How Database Convergence Impacts the Coming Decades of Data Management by Nikita Shamgunov, CEO and co-founder of MemSQL.
Presented at NYC Database Month in October 2017. NYC Database Month is the largest database meetup in New York, featuring talks from leaders in the technology space. You can learn more at http://www.databasemonth.com.
James Burkhart explains how Uber supports millions of analytical queries daily across real-time data with Apollo. James covers the architectural decisions and lessons learned building an exactly-once ingest pipeline storing raw events across in-memory row storage and on-disk columnar storage and a custom metalanguage and query layer leveraging partial OLAP result set caching and query canonicalization. Putting all the pieces together provides thousands of Uber employees with subsecond p95 latency analytical queries spanning hundreds of millions of recent events.
Machines and the Magic of Fast LearningSingleStore
Human-machine interaction is no longer the exclusive province of science fiction. The advance of the internet and connected devices has inspired data scientists to create machine-learning applications to extract value from these new forms of data.
So what's the next frontier?
Join MemSQL Engineer Michael Andrews and Sr. Director Mike Boyarski to learn how to use real-time data as a vehicle for operationalizing machine-learning models. Michael and Mike will explore advanced tools, including TensorFlow, Apache Spark, and Apache Kafka, and compelling use cases demonstrating the power of machine learning to effect positive change.
You will learn:
Top technologies for building the ideal machine-learning stack
How to power machine-learning applications with real-time data
A use case and demo of machine learning for social good
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
2. About Me: Neil Dahlke
Engineer
Formerly Globus
• high performance data transfer for research scientists
Past talks
• Real-time, Geospatial, Maps
Slides: http://www.slideshare.net/MemSQL/realtime-geospatial-
maps-by-neil-dahlke
7. Architecture: It’s SQL All The Way Down
Agg 1 Agg 2
select avg(price) from orders;
leaf1> using memsql_demo_0
select count(1), sum(price)
from orders;
leaf2> using memsql_demo_12
select count(1), sum(price)
from orders;
...
Leaf 1 Leaf 2 Leaf 3 Leaf 4
8. Latency in the Enterprise
SELECT*
FROM
WHERE
SLOW DATA
LOADING
Batched Loading
Hours to load
Sampled Data Views
No real-time ingestion
LENGTHY QUERY
EXECUTION
Slow query responses
Slow reports
Slow applications
No real-time response
LOW CONCURRENCY
Single threaded operations
Challenge with mixed workloads
Overall poor performance
9. REIMAGINE AN EXISTING BUSINESS PROCESS.
What if you had intra-day information to inform your decision making,
instead of daily or even weekly?
13. Why MemSQL?
FAST DATA
INGEST
The volume of data
that can be ingested
into the database
LOW LATENCY
QUERIES
The time it takes to
execute queries and
receive results
HIGH
CONCURRENCY
The ability to scale
simultaneous operations
20. A massively scalable database and ingest solution allowed for
massive growth, real-time analytic applications and faster, targeted.
+
21. Kafka
S3
• Persisted all logs to cold storage for eventual analysis
Hadoop
• Nightly map-reduce jobs
Redshift
• Took a full day to load data from previous day
• Reaching overlap of times caused data crisis
• Pre-aggregated
• Limited concurrency
Before
22. Late data
Limited access to the data once it’s in
Long waits for insight
Expensive
Why was this bad for their business?
23. Why was this bad for their data operations?
Not scalable
No deduplication
• aka not exactly-once
Unfiltered and incomplete data (silos)
Pre-aggregated data
FAST DATA
INGEST
LOW
LATENCY
QUERIES
HIGH
CONCURRENCY
30. Visualizing the Data
Demo built using
• Mapbox
• Websockets
• Tornado web server
When an image is pinned, the circles on the globe
expand, showing higher volume areas
Reads data from MemSQL directly
32. Introducing MemSQL Pipelines
CREATE PIPELINE is a database construct that enables
data ingestion with exactly-once semantics
• MemSQL stores the Kafka offset in a table
• Exactly once delivery facilitated by co-locating data and offsets
Extract, transform, and load external data natively
Fully distributed workloads
User-defined transformations
Scalable, highly performant, online ALTER TABLE and
ALTER PIPELINE
33. MemSQL Pipelines Sequence
1. Extract from data sources
2. Transform extracted data
3. Load transformed data into Database tables in parallel
Data
Sources
MemSQL
1. Extract 2. Transform extracted data 3. Load into Database tables
Pipelines
36. Getting Data to MemSQL
CREATE PIPELINE Streamliner
Parallel loading from multiple sources Parallel loading from multiple sources
Direct to leaf nodes
Data to multiple aggregators, then leaf
nodes
Native feature Built with Apache Spark
Exactly-once semantics
40. Learn More
[ODBMS Watch] Powering Big Data at Pinterest.
Interview with Krishna Gade
[GigaOm] Pinterest is experimenting with MemSQL for
real-time data analytics
[InfoQ] Real-time Data Analytics at Pinterest using
MemSQL and Spark Streaming
[MemSQL Blog] How Pinterest Measures Real-Time User
Engagement with Spark
[Pinterest Engineering Blog] Real-time analytics at
Pinterest