Enterprise Data Lake:
How to Conquer the Data Deluge and Derive Insights
that Matters
Data can be traced from various consumer sources.
Managing data is one of the most serious challenges faced
by organizations today. Organizations are adopting the data
lake models because lakes provide raw data that users can
use for data experimentation and advanced analytics.
A data lake could be a merging point of new and historic
data, thereby drawing correlations across all data using
advanced analytics. A data lake can support the self-service
data practices. This can tap undiscovered business value
from various new as well as existing data sources.
Furthermore, a data lake can aid data warehousing,
analytics, data integration by modernizing. However, lakes
also face hindrances like immature governance, user skills
and security.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
To mesh or mess up your data organisation - Jochem van Grondelle (Prosus/OLX ...Jochem van Grondelle
Recently the concept of a ‘data mesh’ was introduced by Zhamak Deghani to solve architectural and organizational challenges with getting value from data at scale more logically and efficiently, built around four principles:
* Domain-oriented decentralized data ownership
* Data as a product
* Self-serve data infrastructure as a platform
* Federated computational governance
This presentation will initially deep-dive into the ‘data mesh’ and how it fundamentally differs from the typical data lake architectures used today. Subsequently, it describes OLX Europe’s current data platform state aimed partially towards a more decentralized data architecture, covering its analytical data platform, data infrastructure, data discovery, and data privacy.
Finally, it will see to what extent the main principles around the ‘data mesh’ can be applied to a future vision for our data platform and what advantages and challenges implementing such a vision can bring for OLX and other companies.
For more information on data mesh principles, check out the original article by Zhamak: https://martinfowler.com/articles/data-mesh-principles.html.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Snowflake's Kent Graziano talks about what makes a data warehouse as a service and some of the key features of Snowflake's data warehouse as a service.
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Data lakes are central repositories that store large volumes of structured, unstructured, and semi-structured data. They are ideal for machine learning use cases and support SQL-based access and programmatic distributed data processing frameworks. Data lakes can store data in the same format as its source systems or transform it before storing it. They support native streaming and are best suited for storing raw data without an intended use case. Data quality and governance practices are crucial to avoid a data swamp. Data lakes enable end-users to leverage insights for improved business performance and enable advanced analytics.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
To mesh or mess up your data organisation - Jochem van Grondelle (Prosus/OLX ...Jochem van Grondelle
Recently the concept of a ‘data mesh’ was introduced by Zhamak Deghani to solve architectural and organizational challenges with getting value from data at scale more logically and efficiently, built around four principles:
* Domain-oriented decentralized data ownership
* Data as a product
* Self-serve data infrastructure as a platform
* Federated computational governance
This presentation will initially deep-dive into the ‘data mesh’ and how it fundamentally differs from the typical data lake architectures used today. Subsequently, it describes OLX Europe’s current data platform state aimed partially towards a more decentralized data architecture, covering its analytical data platform, data infrastructure, data discovery, and data privacy.
Finally, it will see to what extent the main principles around the ‘data mesh’ can be applied to a future vision for our data platform and what advantages and challenges implementing such a vision can bring for OLX and other companies.
For more information on data mesh principles, check out the original article by Zhamak: https://martinfowler.com/articles/data-mesh-principles.html.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Snowflake's Kent Graziano talks about what makes a data warehouse as a service and some of the key features of Snowflake's data warehouse as a service.
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Data lakes are central repositories that store large volumes of structured, unstructured, and semi-structured data. They are ideal for machine learning use cases and support SQL-based access and programmatic distributed data processing frameworks. Data lakes can store data in the same format as its source systems or transform it before storing it. They support native streaming and are best suited for storing raw data without an intended use case. Data quality and governance practices are crucial to avoid a data swamp. Data lakes enable end-users to leverage insights for improved business performance and enable advanced analytics.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
For Impetus’ White Papers archive, visit- http://www.impetus.com/whitepaper
In this paper, Impetus focuses at why organizations need to design an Enterprise Data Warehouse (EDW) to support the business analytics derived from the Big Data.
Presentation from Data Science Conference 2.0 held in Belgrade, Serbia. The focus of the talk was to address the challenges of deploying a Data Lake infrastructure within the organization.
The main idea of a Data Lake is to expose the company data in an agile and flexible way to the people within the company, but preserve safeguard and auditing features that are required for the company’s critical data. The way that most projects in this direction start out is by depositing all of the data in Hadoop, trying to infer the schema on top of the data and then use the data for analytics purposes via Hive or Spark. Described stack is a really good approach for many use cases, as it provides cheaply storing data in files and rich analytics on top. But many pitfalls and problems might show up on this road, which can be easily met by extending the toolset. The potential bottlenecks will be displayed as soon as the users arrive and start exploiting the Lake. From all of these reasons, planning and building a Data Lake within an organization requires strategic approach, in order to build an architecture that can support it.
Asterix Solution’s Hadoop Training is designed to help applications scale up from single servers to thousands of machines. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
Duration - 25 hrs
Session - 2 per week
Live Case Studies - 6
Students - 16 per batch
Venue - Thane
This article useful for anyone who want to introduce with Big Data and how oracle architecture Big Data solution using Oracle Big Data Cloud solutions .
Digital Hospitals: The Future of Acute Caresambiswal
BIG DATA AND ARTIFICIAL INTELLIGENCE ARE BUILDING A NEW HEALTHCARE LANDSCAPE
Digital Hospitals: The Future of Acute Care - Advances in digital health are changing the medical landscape. By embracing digital transformations, hospitals are able to grant providers access to real-time patient records coupled population health data enhanced by artificial intelligence offering greater insights for improved patient outcomes. With the advancement of wearables and other remote diagnostic and monitoring devices, patients can receive quality care anywhere a connection to the cloud exists.
Healthcare providers now have a whole host of tools and resources at their fingertips to access real-time data, monitor patients remotely and make better care decisions. This access to better data frees the patient from the physical restraints of the hospital environment. This lessens the dependence on admitting patients into large hospitals for extended stays at exorbitant costs to both payers and patients.
Unlock the future of drug intelligence - Scalablehealth.com sambiswal
DRIVERS OF ANALYTICS IN LIFE SCIENCES
Scalable Health is committed to helping life sciences companies improve health outcomes faster than ever. With our focus on delivering tomorrow's technology, life sciences companies can partner with us for innovative solutions that help solve current healthcare challenges and enhance the quality of life. We help pharmaceutical companies discover opportunities to transform their operations and increase their agility.
Integrate healthcare data from various sources to improve the quality of care...sambiswal
With EHR implementation, meaningful use, ACO and HIE interoperability, mergers, and interface engine conversion, the demand for data integration is endless. Scalable Health Data Integration services help healthcare organizations to quickly ingest, prepare and deliver clinical, patient, financial and operational data from diverse sources, whether on-premises or in the cloud. Leveraging heterogeneous datasets and securely linking them has the potential to improve healthcare by identifying the right treatment for the right individual.
Comprehensive Data Archiving and Retention - Scalabledigital.com sambiswal
Comprehensive Data Archiving and Retention Solution for Managing Data Access, Compliance and Improve Performance throughout the Enterprise.
Next-Generation Enterprise Information Archiving Solution For Applications, Databases And Data Warehouses
Scalable StrongRoom Data Archive solution manages the speed, volume and density of data growth for lower storage costs and enhanced performance. It enables organization to control data growth in production databases and retire legacy applications, while managing retention, ensuring compliance and retaining access to business data.
FASTER, BETTER ANSWERS TO REAL-FINANCE PROBLEMS - Scalabledigital.com sambiswal
Scalable Digital analytical services includes reporting, analysis and predictive modeling for financial institutions to measure and meet risk-regulated performance objectives, lower the compliance and regulation cost and foster a risk management culture. From asset and liability product to wealth management product, we offer data-driven analytics services to financial industry for better financial planning and control.
Enhancing Healthcare Member Experience - Insights.scalabledigital.com sambiswal
In the ever-changing landscape of the healthcare industry, insurance companies constantly need to identify ways in which to lower costs and improve member satisfaction for sustainable growth. One way to both reduce costs and enhance customers’ experience is by better understanding a member’s journey. In the healthcare industry, a member’s journey includes every stage a person goes through, from enrollment, to care, to care management and preventive health.
Scalable Systems has developed a solution that tracks and analyzes members’ journey using a value-based model to provide insights on healthcare members on which to base insurance related decisions. To enhance their solution, Scalable Systems uses demographic and geospatial data sets from Pitney Bowes and using APIs, connects to several modules of the Pitney Bowes Spectrum Platform: Universal Addressing Module, Enterprise Geocoding Module, Location Intelligence Module and Enterprise Routing Module. Integrating the data and technologies of both companies will generate deeper member insights faster and with a higher degree of accuracy for the healthcare industry than ever before.
Scalable Health to Speed and Secure Biomedical Analytics - Insights.scalableh...sambiswal
“Healthcare data is becoming more complex, unstructured, and decentralized as it brings into its ambit a growing variety of entities and associated data,” explained Scalable CEO Sam Biswal. “This includes EMRs, hospital discharge records, clinical data, drug prescriptions, state monitoring programs, and NIH directives, plus wearable IoT and social media feeds.” To accelerate and improve the quality, reliability, and granularity of insights from this data, Scalable will integrate (or wrangle) it with the interchangeable CoSort or Hadoop engines in Voracity. “We will also use Voracity to classify, anonymize, and measure the likelihood that PHI can be re-identified,” Biswal added.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
AI - The Next Frontier for Connected Pharmasambiswal
Big pharma has long been challenged with siloed data resulting from drug discovery information, clinical trial results and product marketing research stored separately in decade-old legacy systems. Thus, the pharmaceutical industry is ripe for the actionable insights offered by these advances to offset the growing costs of drug discovery while still meeting the demands of a value-based care model. It is time for a connected approach in the pharmaceutical industry.
Risk Stratification in Mental Health using Big Datasambiswal
Learn how risk stratification tools can help determine the likelihood of future healthcare events and increase early intervention and treatment of at-risk patients.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
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.
1. Enterprise Data Lake:
How to Conquer the Data Deluge and Derive Insights
that Matters
A SCALABLE DIGITAL WHITE PAPER
2. EXECUTIVE SUMMARY ……………………………………………………………………………………………………………………….3
THE NEED OF DATA LAKE …………………………………………………………………………………………………………………..5
KEY BENEFITS OF DATA LAKE ……………………………………………………………………………………………………………..6
DIFFERENCES BETWEEN DATA LAKE AND DATA WAREHOUSE? ………………………………………………………….8
DATA WAREHOUSE GAPS FILLED BY DATA LAKE .……………………………………………………………………………….9
DATA LAKE ARCHITECTURE …………………………………………………………………………………………..………………….11
DATA GOVERNANCE …………………………………………………………………………………………..…………………….…..…14
FUTURE OF DATA LAKE …………………………………………………………………………………………..……………….……….15
CONCLUSION …………………………………………………………………………………………..……………………………………...16
REFERENCES …………..…………………………………………………………………………………………..………………….…...….16
TABLE OF CONTENT
www.scalabledigital.com
3. EXECUTIVE SUMMARY
Data can be traced from various consumer sources.
Managing data is one of the most serious challenges faced
by organizations today. Organizations are adopting the data
lake models because lakes provide raw data that users can
use for data experimentation and advanced analytics.
A data lake could be a merging point of new and historic
data, thereby drawing correlations across all data using
advanced analytics. A data lake can support the self-service
data practices. This can tap undiscovered business value
from various new as well as existing data sources.
Furthermore, a data lake can aid data warehousing,
analytics, data integration by modernizing. However, lakes
also face hindrances like immature governance, user skills
and security.
One among four organizations already have at least one
data lake in production. Another quarter will embrace
production in a year. At this rate analyst not only expect this
trend to last long but also forecast it to speed up
incorporation of innovative data generating technologies in
practice. 79% of users having a lake state that most of the
data is raw with some portion for structured data, and those
portions will grow as they comprehend the lake better.
Managing data is one of the most serious challenges faced
by organizations today. The storage systems need to be
managed individually, thus, making infrastructure and
processes more complex to operate and expensive to
maintain. In addition to storage challenges, organizations
also face many complex issues such as limited scalability,
storage inadequacies, storage migrations, high operational
costs, rising management complication and storage tiring
concerns.
There are two major types of data lakes based on data
platform. Hadoop-based data lakes and relational data lakes.
Hadoop is more usual than relational databases. However,
data lake spans both. The platforms may be on premises, on
clouds, or both. Thus, some data lakes are multiplatform as
well as hybrid.
Though adopting and working on traditional technologies like
data mining and data warehousing is important, it is equally
important to adopt modern capabilities that not only makes it
more evolved but efficient as well. As organizations need to
solve challenges at a faster pace, the need has shifted to
adopt hybrid methods to explore, discuss and present the
data management scenarios. In the present day industries,
ideas like data lake to ease data sharing have erupted, but
with traditional methods like data warehousing the scope for
growth is limited.
A data lake receives data from multiple sources in an
enterprise to store and analyze the raw data in its native
format. In an industry, data lake can handle data ranging
from structured data such as demographic data or semi-
structured data such as pdfs, notes, files to completely
unstructured data such as videos and images. Using data
lake, organizations can dive into possibilities yet to be
explored by enabling data management technology to avoid
the functional shortcomings. With the advancements in data
science, artificial intelligence and machine learning, a data
lake could assist with various efficient working models for
this industry, industry related personnel as well as
specialized capabilities like predictive analysis for future
enhancement.
Although data lake is new face and seems to be in a
primitive state, many industry giants like Amazon, Google
etc. have worked on it. They have processed data in a faster
and reliable manner creating a balanced value chain. For its
deployment, administration, and maintenance, lot of efforts
has to be instilled. As it is a pool of data from various
organization, it has to governed, secured and be scalable at
the same time to avoid it being a dump of unrelated data
silos.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
www.scalabledigital.com
4.
5. A data lake is a centralized data repository that
can store a multitude of data ranging from
structured or semi-structured data to completely
unstructured data. Data lake provides a scalable
storage to handle a growing amount of data and
provides agility to deliver insights faster. A data
lake can store securely any type of data
regardless of volume or format with an unlimited
capability to scale and provides a faster way to
analyze datasets than traditional methods.
A data lake provides fluid data management
fulfilling the requirements of an industry as they
try to rapidly analyze huge volumes of data from
a wide range of formats and extensive sources
in real-time.
A data lake has flat architecture to store data
and schema-on-read access across huge
amounts of information that can be accessed
rapidly. The lake resides in a Hadoop system
mostly in the original structure with no content
integration or modification of the base data. This
helps skilled data scientists to draw insights on
data patterns, disease trends, data abuse,
insurance fraud risk, cost, and improved
outcomes and engagement and many more.
A data lake gives structure to an entity by pulling
out data from all possible sources into a
legitimate and meaningful assimilation. Adopting
data lake, means developing a unified data
model, explicitly working around the existing
system without impacting the business
applications, alongside solving specific business
problems.
However, with every opportunity comes a
challenge. The concept of “Data Lake” is
challenging, the attributing reasons being
• Entities have several linkages across the
enterprise infrastructure and functionality. This
leads to non-existence of a singular
independent model for entities.
• It contains all data, both structured and
unstructured, which enterprise practices might
not support or have the techniques to support.
• It enables users across different units of
enterprise to process, explore and augment data
based on the terms of their specific business
models. Various implementations might have
multiple access practices and storage construct
for all entities
Technology should be able to let organizations
acquire, store, combine, and enrich huge
volumes of unstructured and structured data in
raw format and have the potential to perform
analytics on these huge data in an iterative way.
Data lake may not be a complete shift but rather
an additional method to aid the existing methods
like big data, data warehouse etc. to mine all of
the scattered data across a multitude of sources
opening new gateway to new insights.
THE NEED OF DATA LAKE
www.scalabledigital.com
6. Having understood the need for the data lake and
the business/technology context of its evolution,
important benefits in the following list:
• Scalability: The Hadoop is a framework that
helps in the balanced processing of huge data
sets across clusters of systems using simple
models. It scales up from single server to
thousands, offering local computation and
storage at each node.
Hadoop supports huge clusters maintaining a
constant price per execution bereft of scaling. To
accommodate more one just has to plug in a new
cluster. Hadoop runs the code close to storage
getting massive data sets processed faster.
Hadoop enables data storage from disparate
sources like multimedia, binary, XML and so on.
• High-velocity Data: The data lake uses tools like
Kafka, Flume, Scribe, and Chukwa to acquire
high-velocity data and queue it efficiently.
Further they try to integrate with large volumes
of historical data.
• Structure: The data lake presents a unique
arena where structure like metadata, speech
tagging etc. can be applied on varied datasets
in the same storage with intrinsic detail. This
enables to process the combinatorial data in
advanced analytic scope.
• Storage: The data lake provides iterative and
immediate access to the raw data without pre-
modelling. This offers flexibility to ask questions
and seek enhances analytical insights.
• Schema: The data lake is schema less write
and schema-based read in the data storage
front. This helps to develop up to date patterns
from the data to grasp applicable intelligent
insights without being dependent on the data.
• SQL: Pre-existing PL-SQL scripts could be
reused once the data is stored in the SQL
storage of the data lake. The tools like HAWQ
and IMPALA gives flexibility to process huge
parallel SQL queries while working in parallel
with algorithm libraries like MADLib and SAS
applications. Performing the SQL takes less
time and also consumes less resources inside
the data lake than performing outside.
• Advanced Algorithms: The data lake is
proficient at using the large amount of
understandable data along with advanced
algorithms to acknowledge article of interest to
power up decision making algorithms.
• Administrative Resources: The data lake
reduces the administrative resources needed
for congregating, transforming, drawing, and
analyzing data.
KEY BENEFITS OF DATA LAKE
www.scalabledigital.com
7. DATA LAKE
Audio
Video
Sensor
NEW BUSINESS VALUES
NEW DATA SOURCES
TIME CONSTRAINTSData Lake Helps Business Organizations
Capture, Manage and Analyze all Their Data.
Data Lake Eliminates
Data Lake Embraces
Data Lake Delivers
60%
20%
80%
42%
61%
Reports can Take
Days or Weeks
Businesses Admit
Unstructured Data Hard to Interpret
Industry Information
is Unstructured
Industries Want
Faster Access to Data
Required to Upgrade
Legacy Systems
IT Spending
on Data Storage
Upto
Global Data
Growth
Per Year
2.7
Data
In Digital
Universe
Zettabytes
Data
Lake
Data
Lake
THE COST OF DATA
Data Lake Reduces
GPS
Imaging
8. www.scalabledigital.com
Data Lake Data Warehouse
A data lake can store structured, semi-
structured as well as unstructured data
Data warehouse only accommodates structured
data that is given in a particular model
Data lake contains relevant data, easy to access
and provides operational back-up to the
enterprise
Data warehouse stores data for longer period of
time which can be accessed on demand
In a data lake, data doesn’t need to be modeled
and only raw data needs to be loaded and used
Data in data warehouse needs to be modeled
before processing data and loading it
Data lake has enough processing power to
analyze and process data being accessed
Data warehouse only processes structured data
into a reporting model for reporting and analytics.
Data lake is easy to reconfigure Data warehouse structure is difficult to reconfigure
because the data is highly structured
Data lake costs less to store data, doesn’t need
licensing and is built on Hadoop, an open
source framework
With data warehouse, optimization is time
consuming and is costly. This works well with pre-
existing modeled data but falls flat for new data.
In data lake, data availability can be easily
spotted and integrated for a requirement. Back
tracking of data and data management are
available and easy to implement.
With data warehouse, data availability is tough to
spot and integrate for a particular requirement.
Back tracking of data and data management are
unavailable and cumbersome to implement.
Manual creation of root data is error prone and
time consuming.
James Dixon’s idea of a new architecture known as
‘Data Lake’ developed in 2010, gained quite a bit of
momentum and captivated numerous data driven
industries. It is easily accessible and can store
anything and everything indifferent of its type,
structure etc.
Data lake and data warehouse have different but
valuable characteristics to offer to an enterprise
together.
Clearly data lake and data warehouse are
complementary to each other in an/the enterprise.
Data lake should not be seen as a replacement for
a data warehouse as they are unique in their own
way, having distinct roles in the industry.
The major differences between a Data lake and a
traditional data warehouse are:
DIFFERENCES BETWEEN DATA LAKE AND DATA WAREHOUSE?
9. Data lake supports multiple reporting tools and
has self sufficient capacity. It helps elevate the
performance as it can traverse huge new datasets
without heavy modeling. –Flexibility. It supports
advanced analytics like predictive analytics and
text analytics. This further allows users to process
the data to track history to maintain data
compliance. - Quality. Data lakes allows users to
search and experiment on structured, semi
structured, unstructured, internal, and external
data from variable sources from one secure view
point. - Findability and Timeliness.
There are many challenges to overcome by data
warehouse. The solution that suffices all of the
gaps is the data lake. This helps to secure the
data and work on the data, run analytics, visualize
and report on it. The characteristics of a stable
data lake are as follows:
• Use of Multiple Tools and Products:
Extracting maximum value out of the data lake
requires customized management and
integration that are currently unavailable from
any single open-source platform or commercial
product vendor. The cross-engine integration
necessary for a successful data lake requires
multiple technology stacks that natively support
structured, semi-structured, and unstructured
data types.
• Domain Specification: The data lake must be
tailored to the specific industry. A data lake
customized for biomedical research would be
significantly different from one tailored to
financial services. The data lake requires a
business-aware data-locating capability that
enables business users to find, explore,
understand, and trust the data. This search
capability needs to provide an intuitive means
for navigation, including key word, faceted, and
graphical search. Under the covers, such a
capability requires sophisticated business
ontologies, within which business terminology
can be mapped to the physical data. The tools
used should enable independence from IT so
that business users can obtain the data they
need when they need it and can analyze it as
necessary, without IT intervention.
• Automated Metadata Management: The data
lake concept relies on capturing a robust set of
attributes for every piece of content within the
lake. Attributes like data lineage, data quality,
and usage history are vital to usability.
Maintaining this metadata requires a highly-
automated metadata extraction, capture, and
tracking facility. Without a high-degree of
automated and mandatory metadata
management, a data lake will rapidly become a
data swamp.
• Configurable Ingestion Workflows: In a
thriving data lake, new sources of external
information will be continually discovered by
business users. These new sources need to be
rapidly on-boarded to avoid frustration and to
realize immediate opportunities. A
configuration-driven, ingestion workflow
mechanism can provide a high level of reuse,
enabling easy, secure, and trackable content
ingestion from new sources.
• Integrate with the Existing Environment: The
data lake needs to meld into and support the
existing enterprise data management
paradigms, tools, and methods. It needs a
supervisor that integrates and manages, when
required, existing data management tools, such
as data profiling, data mastering and cleansing,
and data masking technologies.
DATA WAREHOUSE GAPS FILLED BY DATA LAKE
www.scalabledigital.com
10. In a competitive environment,
organizations will have an
advantage who can derive
insights faster from their data.
11. www.scalabledigital.com
Data lake architecture should be flexible and
organization specific. It relies around a
comprehensive understanding of the technical
requirements with sound business skills to
customize and integrate the architecture.
Industries would prefer to build the data lake
customized to their need in terms of the
business, processes and systems.
An evolved way to build a data lake would be to
build an enterprise model taking few factors into
consideration like, organization’s information
systems and the data ownership. It might take
effort but provides flexibility, control, data
definition clarity and partition of entities in an
organization. Data lake’s self-dependent
mechanisms to create process cycle to serve
enterprise data, help them in consuming
applications.
The data lake as composed of three layers and
tiers. Layers are the common functionality that
cut across all the tiers. These layers are:
• Data Governance and Security Layer
• Metadata Layer
• Information Lifecycle Management Layer
Tiers are abstractions for a similar functionality
grouped together for the ease of understanding.
Data flows sequentially through each tier. While
the data moves from tier to tier, the layers do
their bit of processing on the moving data. The
following are the three tiers:
• Intake Tier
• Management Tier
• Consumption Tier
One major architecture defining data lake
architecture is the Lambda architecture pattern.
This architecture makes the data lake fault
tolerant, data immutable and helps in re-
computation.
The CAP theorem, also named as Brewer's
theorem, states that a distributed data store
cannot simultaneously provide more than two
out of the following three:
• Consistency
• Availability
• Partition tolerance.
The data lake with Lambda Architecture’s aid,
works with the CAP theorem on a contextual
basis. The three major contributions of the CAP
theorem are Consistency, Availability and
Partition tolerance. Usually availability is chosen
over consistency because consistency can be
achieved eventually. If not most data goes with
approximations.
DATA LAKE ARCHITECTURE
12. www.scalabledigital.com
DAaaS (Data Analytics-as-a-Service) is a
protractible platform. It uses a cloud-based
delivery model. It provides a wide range of
tools to select from for data analytics that can
be designed by the user to process large
amounts of data effectively. Enterprise data is
ingested into the platform. Further the data is
processed by analytics applications. This
could provide business insight using
advanced analytical algorithms and machine
learning
As per researchers, experts and data
enthusiasts, the “Data Lake” to “a successful
Data and Analytics” transformation needs the
following:
• DAaaS Strategy Service Definition: Our
Informationists leverage define the catalog
of services to be provided by the DAaaS
platform, including data onboarding, data
cleansing, data transformation, datapedias,
analytic tool libraries, and others.
• DAaaS Architecture: We help our clients
achieve a target-state DAaaS architecture,
including architecting the environment,
selecting components, defining engineering
processes, and designing user interfaces.
• DAaaS PoC: We design and execute
Proofs-of-Concept (PoC) to demonstrate
the viability of the DAaaS approach. Key
capabilities of the DAaaS platform are
built/demonstrated using leading-edge
bases and other selected tools.
• DAaaS Operating Model Design and
Rollout: We customize our DAaaS
operating models to meet the individual
client’s processes, organizational structure,
rules, and governance. This includes
establishing DAaaS chargeback models,
consumption tracking, and reporting
mechanisms.
• DAaaS Platform Capability Build-Out: We
provide the expertise to conduct an iterative
build-out of all platform capabilities,
including design, development and
integration, testing, data loading, metadata
and catalog population, and rollout.
13. INGESTION
EXECUTION
DATA
HDFS
CONSUMPTION
Application & Analytical
Workspaces
Mobile Collaborate Analyze Enterprise
DATA LAKE REFERENCE ARCHITECTURE
www.scalabledigital.com
STRUCTURED / UNSTRUCTURED
Social MediaDigital Imaging
Machine Learning
OLAP
Data Lake Analytics NoSQL In-Memory
RDBMS
Weather Communication Machine log
SensorLocationERPCRM WearableEmail
14. Data Governance is discipline that is enforced on
data by an organization as it moves from input to
output, making sure that it is not rigged in any way
that is risky. To meet the strategic goals, an
organization has to convert ingested data into
intelligence on a fast pace and accurate basis. It
strengthens the decision making process since the
data is adhering to certain quality standards. This
has a huge effect in enhancing the final value of
data, enabling optimal performance planning by
data management staff and minimizing rework.
Data Governance deals with processes to lay
down the technology architecture to help store and
manage mass data. Further, Data Governance
deals with the right security policy of the data as it
is being acquired and as it flows through the
enterprise. While the data is worked upon to derive
a new form, Data governance assures the integrity
and accuracy is not meddled with. To maintain and
prevent shortage of storage space, data past its
usage date is moved to tape storage or is
defensively destroyed. This process is owned by
Information Lifecycle Management policies, a
subset of Data Governance processes.
Organization without a strong Data Governance
process end up jeopardizing the caliber of analytics
and decisions deduced from it. This exposes the
organization to a substantial risk. On the other
hand, organizations having a strong built Data
Governance processes have arrangements to
improve Data Security with intrinsic authentication
and authorization in place. In addition, they also
have data loss guarding systems.
The basic components of Data Governance that
cuts across the Data Intake, management, and
consumption tiers of the Data Lake are:
• Metadata management
• Lineage tracking
• Data Security and privacy
• Information Lifecycle Management components
www.scalabledigital.com
DATA GOVERNANCE
Data Steward
Project Team
• Acts on Requirements
• Build Capabilities
• Does the Work
• Responsible for Adherence
• Highlights Work
• Drives Change Accountable
for results
• Executive Oversight
Enterprise
Data Council
Governance
Committee
Data Integrity Stewardship
Principles and
Standards
Information
Accountabilities
Definitions
Metrics Control
Mechanisms
Architecture
Information
Usability
Risk/Reward
Enterprise
Data Council
15. The Data Lake deals with the storage,
management, and analytical aspects related with
the facets of Big Data. Monitoring the Big Data
using the Data Lake adhering to the existing data
governance methods to deal with Big Data and
also cut a space for expansion of emerging
trends.
In the future, it is expected that organizations' data
analytics methodologies would evolve to fetch the
following abilities:
• The demand for extreme high-speed insights
• The growth of Internet of Things
• The adoption of cloud technologies
• The evolution of deep learning.
Deep learning is typically used to solve some of
the intractable problems of Big Data as follows:
• Semantic indexing
• Classifying unlabeled text
• Data tagging
• Entity recognition and natural language
processing
• Image classification and computer vision
• Fast information retrieval
• Speech recognition
• Simplifying discriminative tasks
Data Lake possesses the optimal balance to
enable organizations to tap the real benefits of
advanced analytics and Deep Learning methods.
The Data Lake property to store data in a schema
less way is immensely useful for the methods to
extract unstructured data representations. Data
Lake also has the ability to whisk complicated
high end deep learning algorithms on elevated-
dimensional and streaming data.
FUTURE OF DATA LAKE
www.scalabledigital.com
16. Data lakes with advanced analytics are
reshaping the way enterprises work. Future with
data lakes looks very promising. System
developers are immersed in vigorous R&D for
such technology advancement for better
analysis and detail oriented search. It could be
useful for industries by providing better
efficiency and outcomes.
To be at an advantage, industry will have to use
the power of data lake driven processes and
systems. If fathomed intuitively, it could change
the way services is being delivered.
Presently, data lake practices are governed by
Hadoop predominantly. Hadoop has become
the major tool for assimilating and pulling out
insights from combinatorial unstructured data
present in Hadoop and enterprise data assets,
running algorithms in batch mode using the
MapReduce paradigm. Hadoop, with the
existing enterprise data assets such as data in
mainframes and data warehouses. Languages
such as Pig, Java Map Reduce, SQL variants, R,
Hadoop, Apache Spark, and Python are being
increasingly used for data munging, data
integration, data cleansing, and running
distributed analytics algorithms.
There is more to consider with details including:
big data architecture for accessible Data Lake
infrastructure, data lake functionality, solving
data accessibility and integration at enterprise
level, data flows in the data lake, and many
more. With these numerous queries, there still
is resources to tap and a lot to gain for the
enterprise. Using the data lake architecture to
derive cost efficient, life-changing insights from
the huge mass of data nullifies the concern
regarding going further with the ice-berg hidden
under the ocean.
www.scalabledigital.com
CONCLUSION
• https://tdwi.org/articles/2017/03/29/executive-summary-data-lakes.aspx
• Data Lake Development with Big Data by Beulah Salome Purra, Pradeep Pasupuleti
http://www.datasciencecentral.com/profiles/blogs/9-key-benefits-of-data-lake
• https://www.blue-granite.com/blog/bid/402596/top-five-differences-between-data-lakes-and-
data-warehouses
REFERENCES