Beyond Kerberos and Ranger - Tips to discover, track and manage risks in hybrid data environments

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Even after deploying traditional security measures like authentication and authorization to secure sensitive data, data owners and security teams are still struggling to manage and get visibility on risks with data. The same challenge multiplies when data is moving and shared across different data silos such as on-premise Hadoop, public cloud infrastructures such as AWS, Azure and Google Cloud. To control the risks that come with data, enterprises need a comprehensive data-centric approach to easily identify risks, manage security and compliance policies and implement behavior analytics to differentiate between good and bad behavior. This talk will explain a 3 step process of implementing data-centric controls for your hybrid environment including discovering where sensitive data is stored, tracking where data is moving and can easily identifying and controlling potential misuse of the data in near real time.

Tecnologia

Beyond Kerberos and
Ranger – Tips to Discover,
Track and Manage Risks in
Hybrid Data Environments
Balaji Ganesan
Don Bosco Durai
DataWorks Summit – San
Jose 2017

Balaji Ganesan
@privacera
Apache Ranger Committer
Don Bosco Durai
@privacera
Apache Ranger, HAWQ, Ambari
committer

Agenda
• Current Challenges
• Privacera Overview
• Demo
• Questions

$2.1 Trillion
Cost of Data Breaches by 2019
4%
of revenues as penalty for GDPR
DATA IS UNDER ATTACK

Current Data Environments
DB
AWS
HDFS
Hive Spark HBase
S3
Redshift
Dynamo
DB

Security State in Hadoop
Kerberos Ranger Knox
HDFS
Encryption

Security State in AWS
VPC
Security
Groups
IAM
AWS
Encryption

Current Challenges – Beyond Kerberos and
Ranger
Any sensitive
data being
ingested?
1
What are users
doing after
access?
2
Visibility on
malicious use or
compliance
adherence
3

Challenges – Where is sensitive data?
Sensitive data
could be
hidden within
data

Challenges – What are users doing with data?
Distcp between HDP Environment and S3 in cloud – 137 log
entries for single transaction

Challenges – Identifying data loss or compliance
adherence

How you can address these challenges?
Discover- Invest in
sensitive data
discovery solution
1
Control- Build
classification based
controls and policies
2
Monitor - Invest in
behavioral monitoring
solutions (trust your
users, but verify)
3

Discovery
• Automatically discover and
classify any sensitive content,
structured or unstructured
data
• Machine learning, NLP to
understand context
• Work with external metadata
systems
• Understand risks with data
stored in the data lake
• Make control decisions based
on the content of the data
What it is? How does it benefit ?

Control
• Store metadata in Atlas,
leverage Atlas-Ranger
integration
• Configure policies in Ranger
to enable access to data
based on content. Configure
IAM similarly
• Anonymize or encrypt data
based on classification
• Control access based on
classification, focus on
sensitive data
• Dynamically control
anonymization or encryption
based on content of data
What it is? How does it benefit ?

Monitor
• Once access is granted,
monitor how users are using
sensitive data
• Track if sensitive data is being
downloaded or moved out of
restricted zones
• Detect and prevent data
misuse, compliance violations
• Enable trust in the
environment and broaden
use-cases
• “Trust the users, but verify”
• Provide real time visibility to
compliance and security team
What it is? How does it benefit ?

Enterprise Wide Policies
Name SSN Credit Card
Raw HDFS Files Yes Yes Yes
Hive Columns Yes Yes Yes
1. Michael is a privileged user with access to SSN and Credit Cards
2. Jennifer can only see the last 4 digits of the SSN and masked Credit Card
numbers
Name SSN Credit Card
Raw HDFS Files Yes No No
Hive Columns Yes Last 4 digits Masked
Michael (Privileged User)
Jennifer (Contractor)
Credit Card Info (Name, SSN, Credit Card #)

Summary
• Risks are increasing with growth of data, number of applications and
users
• Build basic security controls leveraging security in HDP and other
environments
• Build measures to address risks with data movement, and potential
data misuse and leakage
• Visibility into security and compliance violations. Build templates for
specific compliance needs

Questions ?
balaji@privacera.com
bosco@privacera.com

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Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.

HBase Tales From the Trenches - Short stories about most common HBase operati...

DataWorks Summit

LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.

Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...

DataWorks Summit

Managing the Dewey Decimal System

DataWorks Summit

Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL. Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist). In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.

Practical NoSQL: Accumulo's dirlist Example

DataWorks Summit

Data serves as the platform for decision-making at Uber. To facilitate data driven decisions, many datasets at Uber are ingested in a Hadoop Data Lake and exposed to querying via Hive. Analytical queries joining various datasets are run to better understand business data at Uber. Data ingestion, at its most basic form, is about organizing data to balance efficient reading and writing of newer data. Data organization for efficient reading involves factoring in query patterns to partition data to ensure read amplification is low. Data organization for efficient writing involves factoring the nature of input data - whether it is append only or updatable. At Uber we ingest terabytes of many critical tables such as trips that are updatable. These tables are fundamental part of Uber's data-driven solutions, and act as the source-of-truth for all the analytical use-cases across the entire company. Datasets such as trips constantly receive updates to the data apart from inserts. To ingest such datasets we need a critical component that is responsible for bookkeeping information of the data layout, and annotates each incoming change with the location in HDFS where this data should be written. This component is called as Global Indexing. Without this component, all records get treated as inserts and get re-written to HDFS instead of being updated. This leads to duplication of data, breaking data correctness and user queries. This component is key to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. This component will need to have strong consistency and provide large throughputs for index writes and reads. At Uber, we have chosen HBase to be the backing store for the Global Indexing component and is a critical component in allowing us to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. In this talk, we will discuss data@Uber and expound more on why we built the global index using Apache Hbase and how this helps to scale out our cluster usage. We’ll give details on why we chose HBase over other storage systems, how and why we came up with a creative solution to automatically load Hfiles directly to the backend circumventing the normal write path when bootstrapping our ingestion tables to avoid QPS constraints, as well as other learnings we had bringing this system up in production at the scale of data that Uber encounters daily.

HBase Global Indexing to support large-scale data ingestion at Uber

DataWorks Summit

Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions. These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.

Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix

DataWorks Summit

Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms. To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.

Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi

DataWorks Summit

Supporting Apache HBase : Troubleshooting and Supportability Improvements

DataWorks Summit

Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores. With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.

Presto: Optimizing Performance of SQL-on-Anything Engine

DataWorks Summit

Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.

Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...

DataWorks Summit

Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.

Extending Twitter's Data Platform to Google Cloud

DataWorks Summit

At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.

Event-Driven Messaging and Actions using Apache Flink and Apache NiFi

DataWorks Summit

Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.

Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger

DataWorks Summit

Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.

Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...

DataWorks Summit

Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as: ● Optimizing merchandising execution, in-stocks and sell-thru ● Enhancing operational efficiencies, enable real-time customer engagement ● Enhancing loss prevention capabilities, response time ● Creating frictionless experiences for shoppers Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry. We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey. Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables. We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance. We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing. Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems. By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.

Computer Vision: Coming to a Store Near You

DataWorks Summit

Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.

Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark

DataWorks Summit

The Census Bureau is the U.S. government's largest statistical agency with a mission to provide current facts and figures about America's people, places and economy. The Bureau operates a large number of surveys to collect this data, the most well known being the decennial population census. Data is being collected in increasing volumes and the analytics solutions must be able to scale to meet the ever increasing needs while maintaining the confidentiality of the data. Past data analytics have occurred in processing silos inhibiting the sharing of information and common reference data is replicated across multiple system. The use of the Hortonworks Data Platform, Hortonworks Data Flow and other open-source technologies is enabling the creation of a cloud-based enterprise data lake and analytics platform. Cloud object stores are used to provide scalable data storage and cloud compute supports permanent and transient clusters. Data governance tools are used to track the data lineage and to provide access controls to sensitive data.

Transforming and Scaling Large Scale Data Analytics: Moving to a Cloud-based ...

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Floating on a RAFT: HBase Durability with Apache Ratis

Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi

HBase Tales From the Trenches - Short stories about most common HBase operati...

Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...

Managing the Dewey Decimal System

Practical NoSQL: Accumulo's dirlist Example

HBase Global Indexing to support large-scale data ingestion at Uber

Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix

Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi

Supporting Apache HBase : Troubleshooting and Supportability Improvements

Presto: Optimizing Performance of SQL-on-Anything Engine

Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...

Extending Twitter's Data Platform to Google Cloud

Event-Driven Messaging and Actions using Apache Flink and Apache NiFi

Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger

Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...

Computer Vision: Coming to a Store Near You

Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark

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Beyond Kerberos and Ranger - Tips to discover, track and manage risks in hybrid data environments

1. Beyond Kerberos and Ranger – Tips to Discover, Track and Manage Risks in Hybrid Data Environments Balaji Ganesan Don Bosco Durai DataWorks Summit – San Jose 2017

2. Balaji Ganesan @privacera Apache Ranger Committer Don Bosco Durai @privacera Apache Ranger, HAWQ, Ambari committer

3. Agenda • Current Challenges • Privacera Overview • Demo • Questions

4. $2.1 Trillion Cost of Data Breaches by 2019 4% of revenues as penalty for GDPR DATA IS UNDER ATTACK

5. Current Data Environments DB AWS HDFS Hive Spark HBase S3 Redshift Dynamo DB

6. Security State in Hadoop Kerberos Ranger Knox HDFS Encryption

7. Security State in AWS VPC Security Groups IAM AWS Encryption

8. Current Challenges – Beyond Kerberos and Ranger Any sensitive data being ingested? 1 What are users doing after access? 2 Visibility on malicious use or compliance adherence 3

9. Challenges – Where is sensitive data? Sensitive data could be hidden within data

10. Challenges – What are users doing with data? Distcp between HDP Environment and S3 in cloud – 137 log entries for single transaction

11. Challenges – Identifying data loss or compliance adherence

12. How you can address these challenges? Discover- Invest in sensitive data discovery solution 1 Control- Build classification based controls and policies 2 Monitor - Invest in behavioral monitoring solutions (trust your users, but verify) 3

13. Discovery • Automatically discover and classify any sensitive content, structured or unstructured data • Machine learning, NLP to understand context • Work with external metadata systems • Understand risks with data stored in the data lake • Make control decisions based on the content of the data What it is? How does it benefit ?

14. Control • Store metadata in Atlas, leverage Atlas-Ranger integration • Configure policies in Ranger to enable access to data based on content. Configure IAM similarly • Anonymize or encrypt data based on classification • Control access based on classification, focus on sensitive data • Dynamically control anonymization or encryption based on content of data What it is? How does it benefit ?

15. Monitor • Once access is granted, monitor how users are using sensitive data • Track if sensitive data is being downloaded or moved out of restricted zones • Detect and prevent data misuse, compliance violations • Enable trust in the environment and broaden use-cases • “Trust the users, but verify” • Provide real time visibility to compliance and security team What it is? How does it benefit ?

16. Demo Privacera + Atlas + Ranger

17. Enterprise Wide Policies Name SSN Credit Card Raw HDFS Files Yes Yes Yes Hive Columns Yes Yes Yes 1. Michael is a privileged user with access to SSN and Credit Cards 2. Jennifer can only see the last 4 digits of the SSN and masked Credit Card numbers Name SSN Credit Card Raw HDFS Files Yes No No Hive Columns Yes Last 4 digits Masked Michael (Privileged User) Jennifer (Contractor) Credit Card Info (Name, SSN, Credit Card #)

18. Summary • Risks are increasing with growth of data, number of applications and users • Build basic security controls leveraging security in HDP and other environments • Build measures to address risks with data movement, and potential data misuse and leakage • Visibility into security and compliance violations. Build templates for specific compliance needs

19. Questions ? balaji@privacera.com bosco@privacera.com

Notas do Editor

The risks come from data breaches and privacy penalties. Hackers are after your social security numbers, credit cards and other personal data. The costs of data breaches are growing exponentially with estimated costs to $2.1 trillion by 2019. On the other hand, privacy regulations are on the rise with a new regulation GDPR imposing upto 4% of revenues as penalties

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Beyond Kerberos and Ranger - Tips to discover, track and manage risks in hybrid data environments

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