Welcome!

@DXWorldExpo Authors: Liz McMillan, Pat Romanski, Yeshim Deniz, Elizabeth White, Ed Featherston

Related Topics: @DXWorldExpo, Java IoT, @CloudExpo

@DXWorldExpo: Article

Data Lake Plumbers | @BigDataExpo @Schmarzo #BigData #IoT #AI #ML #DL

The data lake is ideal for your data science team as it liberates them from the constraints & limitations of the data warehouse

Many of my blogs promote the business benefits of the data lake, both from a “save me more money” as well as the “make me more money” perspectives. But I fear that I’m making this thing called the data lake sound like a “silver bullet" [1] – just drop the data into the data lake and everything magically works. But much like in the world of data warehousing, there is significant work that needs to be done under the covers – in areas such as metadata management, data governance and security – to ensure that the data lake will perform for a business in a production environment. Many of the processes and techniques we learned in the data warehouse will benefit us here, though there are many new tools to be aware of that can help us in the operationalization task.

I’ve asked an industry expert in metadata management and data governance, Joe DosSantos (follow Joe on twitter: @JoeDosSantos) to co-author this blog with me. Well, to be honest, this mostly reflects Joe’s experience and thinking; I just wanted to get credit for being smart enough to know when to bring someone smarter than me into the conversation!

Data Lake Benefits
You know from previous blogs that there are many benefits to the data lake including:

  • Capture data from wide range of traditional (operational, transactional) and new sources (structured and unstructured) as-is
  • Store all your data in one environment for cross-functional business analysis
  • Support the analytics and data science to uncover new customer, product, and operational insights
  • Empower front-line employees and managers, and drive a more profitable customer engagement leveraging customer, product and operational insights
  • Integrate analytic insights into operational (Finance, Manufacturing, Marketing, Sales Force, Procurement, Logistics) and management systems (Business Intelligence reports and dashboards)

The data lake is ideal for your data science team in that it liberates them from the constraints and limitations of the data warehouse, enabling the data science team to quickly ingest, test and determine if there is any value to different data sets and analytic techniques without having to go through the rigorous operational procedures that govern the data warehouse.

However, this liberty can be quite terrifying in highly regulated environments. Companies have spent years developing governance and stewardship organizations specifically to control patient information, personal contact information, account balances, and other sensitive information. The description above seems to undo all of this work by creating free and easy access to data that should be locked down.

This is why the controls of a data lake need to be very clear. Data that is onboarded into a lake must go through a rigorous set of operational procedures to manage and govern that data set to make sure that it is appropriately tagged and protected, and then provisioned only to people who have the proper authorization. Modern data tools allow for this kind of governance capability to balance the quick and easy access to data that a data scientist needs with the security that good practices (and often the government) demand.

Operationalizing the Data Lake
Operationalizing the data lake requires several non-obvious disciplines, many of which we learned from our data warehouse experiences. These disciplines include data ingestion, indexing, cataloging, metadata management, data governance and security [2].

  1. As with a data warehouse, you will need a method to bring data into your environment. As batch windows became longer and longer in the data warehouse world and business users clamored for increasingly up-to-date information, practitioners began shifting from conventional data ETL (Extract, Transform, Load) to lower latency streaming and micro-batch. This trend was extended in the big data universe with tools like Kafka, a streaming message bus, and with Spring and Sqoop to accelerate data ingest. In the big data world, you might also want to ingest unstructured data sets as well, introducing new tools like Flume. Finally, you may want to trigger complex events based on this data stream and you might do so via Spark, Gemfire, or other in-memory grids. And just to make it more complex, you will likely use several of these tools in combination depending on your data feed needs. Keep in mind that in the world of ELT (Extract, Load, Transform) (note that the order differs from E-T-L), most of these data movements are data dumps. At this point, you have simply collected lots of raw data. It’s now time to make sense of it.
  2. Next, it is useful to tag files that you have ingested. What kind of file is this? What would be useful to know about it so that I could search for it later? Zaloni Bedrock is an example of a tool to apply metadata tags to the files, which is useful for both structured and unstructured data sets.
  3. We mentioned above that one of the key requirements of our data lake is having control over who can have access to specific data sources. Generally speaking, the data loaded in steps 1 and 2 is what we call “Bronze” data, meaning that it is good enough for the business process that created it. Data in these sets will likely be sensitive and your security should reflect it. However, we need to determine rules for how the data should be modified, obfuscated, or deleted in order to make it consumable for broader audience, or what we might call “Silver” status. You need to create business rules to manage data (e.g. birthdays should be masked and social security numbers should be stored as only the last 4). Collibra is an example of a tool for this rules definition and management. It allows data rules to be set up based on logical business entities by business people rather than technologists.
  4. For those people who are familiar with governance concepts, you will recognize the difference between a policy and a control. A policy is like a speed limit sign along the highway. The control is the police officer that pulls you over if you are driving over that speed limit. Data works the same way. While Collibra establishes the policy, you need to create a method for enforcing that policy. To do this, you need to find the logical entities buried in the data (i.e. “oh look, I found a social security number!”). Examples of such products include Global IDs for scanning structured data sets with the operational systems and Waterline for scanning data inside of Hadoop.
  5. Once you have found the data that you want, you want to implement the rules. For this, there is an open source tool called Atlas that contains an orchestration capability called Falcon that helps implement the rules.
    1. Apache Atlas is a scalable and extensible set of core foundational governance services that enables enterprises to effectively and efficiently meet their compliance requirements within Hadoop and allows integration with the complete enterprise data ecosystem.
    2. Apache Falcon is a data governance engine that defines, schedules, and monitors data management policies. Falcon allows Hadoop administrators to centrally define their data pipelines, and then Falcon uses those definitions to auto-generate workflows in Apache Oozie
  6. Now that the data is loaded, you will want to enforce security through your LDAP capability or possibly through Kerberos. There are also tools like Blue Talon that simplify the ability to authorize, provision, protect, enforce and audit data security policies across your data lake.
  7. Finally, audit controls are critical. Cloudera introduced Navigator specifically to allow simple transparency to data history and lineage. Hortonworks will rely on Atlas to provide this capability.

Data that has gone through the above processes creates a view and accessibility of the data that can be made available to a wide set of users – both business analysts and data science teams.

Summary
When you build a house, the vast majority of the creative work is in the features and curbside appeal. That’s the fun part. But without the underlying plumbing, the house would quickly degrade into a money pit.

Consider the metaphor of a retail store: stocking the shelves vs. purchasing goods. When you go to the store, you don’t care about how the goods got there, but the rules for accessing the goods are everywhere. Cigarettes are behind the front desk. Pharmaceuticals must be dispensed with a prescription. Razor blades are under lock and key (for some strange reason). There are policies and enforcements on stocking the shelves so that the shopping experience is clear and easy.

To successfully operationalize the data lake, organizations need to address all of the plumbing requirements outlined in this blog that enable the business users and data science teams to have confidence in the wealth of data that the organization is amassing. The data lake plumbing processes may not be very glamorous, but without them, you’ll end up with a stinky data dump instead of a glorious data lake.

References:

  1. A “silver bullet” is a simple and seemingly magical solution to a complicated problem.
  2. While I mention several tools, this blog is not meant to be an endorsement of these tools nor is this intended to be a comprehensive list of such tools. However, many of these tools are the same tools that we use in our data lake implementations at EMC.

Data Lake Plumbers: Operationalizing the Data Lake
Bill Schmarzo

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice.

As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

@BigDataExpo Stories
Evan Kirstel is an internationally recognized thought leader and social media influencer in IoT (#1 in 2017), Cloud, Data Security (2016), Health Tech (#9 in 2017), Digital Health (#6 in 2016), B2B Marketing (#5 in 2015), AI, Smart Home, Digital (2017), IIoT (#1 in 2017) and Telecom/Wireless/5G. His connections are a "Who's Who" in these technologies, He is in the top 10 most mentioned/re-tweeted by CMOs and CIOs (2016) and have been recently named 5th most influential B2B marketeer in the US. H...
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
DXWorldEXPO LLC, the producer of the world's most influential technology conferences and trade shows has announced the 22nd International CloudEXPO | DXWorldEXPO "Early Bird Registration" is now open. Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)
Join IBM November 1 at 21st Cloud Expo at the Santa Clara Convention Center in Santa Clara, CA, and learn how IBM Watson can bring cognitive services and AI to intelligent, unmanned systems. Cognitive analysis impacts today’s systems with unparalleled ability that were previously available only to manned, back-end operations. Thanks to cloud processing, IBM Watson can bring cognitive services and AI to intelligent, unmanned systems. Imagine a robot vacuum that becomes your personal assistant tha...
I think DevOps is now a rambunctious teenager - it's starting to get a mind of its own, wanting to get its own things but it still needs some adult supervision," explained Thomas Hooker, VP of marketing at CollabNet, in this SYS-CON.tv interview at DevOps Summit at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
"With Digital Experience Monitoring what used to be a simple visit to a web page has exploded into app on phones, data from social media feeds, competitive benchmarking - these are all components that are only available because of some type of digital asset," explained Leo Vasiliou, Director of Web Performance Engineering at Catchpoint Systems, in this SYS-CON.tv interview at DevOps Summit at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
The “Digital Era” is forcing us to engage with new methods to build, operate and maintain applications. This transformation also implies an evolution to more and more intelligent applications to better engage with the customers, while creating significant market differentiators. In both cases, the cloud has become a key enabler to embrace this digital revolution. So, moving to the cloud is no longer the question; the new questions are HOW and WHEN. To make this equation even more complex, most ...
"This week we're really focusing on scalability, asset preservation and how do you back up to the cloud and in the cloud with object storage, which is really a new way of attacking dealing with your file, your blocked data, where you put it and how you access it," stated Jeff Greenwald, Senior Director of Market Development at HGST, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Creating replica copies to tolerate a certain number of failures is easy, but very expensive at cloud-scale. Conventional RAID has lower overhead, but it is limited in the number of failures it can tolerate. And the management is like herding cats (overseeing capacity, rebuilds, migrations, and degraded performance). In his general session at 18th Cloud Expo, Scott Cleland, Senior Director of Product Marketing for the HGST Cloud Infrastructure Business Unit, discussed how a new approach is neces...
"ZeroStack is a startup in Silicon Valley. We're solving a very interesting problem around bringing public cloud convenience with private cloud control for enterprises and mid-size companies," explained Kamesh Pemmaraju, VP of Product Management at ZeroStack, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
Cloud-enabled transformation has evolved from cost saving measure to business innovation strategy -- one that combines the cloud with cognitive capabilities to drive market disruption. Learn how you can achieve the insight and agility you need to gain a competitive advantage. Industry-acclaimed CTO and cloud expert, Shankar Kalyana presents. Only the most exceptional IBMers are appointed with the rare distinction of IBM Fellow, the highest technical honor in the company. Shankar has also receive...
Agile has finally jumped the technology shark, expanding outside the software world. Enterprises are now increasingly adopting Agile practices across their organizations in order to successfully navigate the disruptive waters that threaten to drown them. In our quest for establishing change as a core competency in our organizations, this business-centric notion of Agile is an essential component of Agile Digital Transformation. In the years since the publication of the Agile Manifesto, the conn...
Business professionals no longer wonder if they'll migrate to the cloud; it's now a matter of when. The cloud environment has proved to be a major force in transitioning to an agile business model that enables quick decisions and fast implementation that solidify customer relationships. And when the cloud is combined with the power of cognitive computing, it drives innovation and transformation that achieves astounding competitive advantage.
"Software-defined storage is a big problem in this industry because so many people have different definitions as they see fit to use it," stated Peter McCallum, VP of Datacenter Solutions at FalconStor Software, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading is another field that can be viewed as social science with a lot of data. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or in making chat-bots. In his session at 20th Cloud Expo, Gaurav Chakravorty, co-founder and Head of Strategy Development ...
Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at Cloud Expo, Ed Featherston, a director and senior enterprise architect at Collaborative Consulting, discussed the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine.
As organizations shift towards IT-as-a-service models, the need for managing and protecting data residing across physical, virtual, and now cloud environments grows with it. Commvault can ensure protection, access and E-Discovery of your data – whether in a private cloud, a Service Provider delivered public cloud, or a hybrid cloud environment – across the heterogeneous enterprise. In his general session at 18th Cloud Expo, Randy De Meno, Chief Technologist - Windows Products and Microsoft Part...
"Cloud computing is certainly changing how people consume storage, how they use it, and what they use it for. It's also making people rethink how they architect their environment," stated Brad Winett, Senior Technologist for DDN Storage, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
Detecting internal user threats in the Big Data eco-system is challenging and cumbersome. Many organizations monitor internal usage of the Big Data eco-system using a set of alerts. This is not a scalable process given the increase in the number of alerts with the accelerating growth in data volume and user base. Organizations are increasingly leveraging machine learning to monitor only those data elements that are sensitive and critical, autonomously establish monitoring policies, and to detect...
In his keynote at 18th Cloud Expo, Andrew Keys, Co-Founder of ConsenSys Enterprise, provided an overview of the evolution of the Internet and the Database and the future of their combination – the Blockchain. Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settl...