Data lakes are centralized data repositories. Data needed by data scientists is physically copied to a data lake which serves as a one storage environment. This way, data scientists can access all the data from only one entry point – a one-stop shop to get the right data. However, such an approach is not always feasible for all the data and limits it’s use to solely data scientists, making it a single-purpose system.
So, what’s the solution?
A multi-purpose data lake allows a broader and deeper use of the data lake without minimizing the potential value for data science and without making it an inflexible environment.
Attend this session to learn:
• Disadvantages and limitations that are weakening or even killing the potential benefits of a data lake.
• Why a multi-purpose data lake is essential in building a universal data delivery system.
• How to build a logical multi-purpose data lake using data virtualization.
Do not miss this opportunity to make your data lake project successful and beneficial.
Today's enterprises need broader access to data for a wider array of use cases to derive more value from data and get to business insights faster. However, it is critical that companies also ensure the proper controls are in place to safeguard data privacy and comply with regulatory requirements.
What does this look like? What are best practices to create a modern, scalable data infrastructure that can support this business challenge?
Zaloni partnered with industry-leading insurance company AIG to implement a data lake to tackle this very problem successfully. During this webcast, AIG's VP of Global Data Platforms, Carlos Matos, and Zaloni CEO, Ben Sharma will share insights from their real-world experience and discuss:
- Best practices for architecture, technology, data management and governance to enable centralized data services
- How to address lineage, data quality and privacy and security, and data lifecycle management
- Strategies for developing an enterprise-wide data lake service for advanced analytics that can bridge the gaps between different lines of business, financial systems and drive shared data insights across the organization
Consumers are engaging with brands across multiple touchpoints, channels, and devices, generating massive amounts of valuable data. Organizations are quickly adopting a number of solutions to keep up with this explosion of customer data and better capture and correlate user behavior.
Two common solutions brands are leveraging to house and analyze all of this customer data are Enterprise Data Warehouses (EDW) and Data Lakes. Register now for this 30-minute webinar and learn:
- Key benefits of each and which is best for your brand
- Why pairing your enterprise data storage solution with customer data initiatives makes your tech stack even more powerful
- How an automated data supply chain fits in a modern EDW and data lake environment
- And more!
The webinar will conclude with a live Q&A Chat with questions from the audience on all things enterprise data storage.
As data analytics becomes more embedded within organizations, as an enterprise business practice, the methods and principles of agile processes must also be employed.
Agile includes DataOps, which refers to the tight coupling of data science model-building and model deployment. Agile can also refer to the rapid integration of new data sets into your big data environment for "zero-day" discovery, insights, and actionable intelligence.
The Data Lake is an advantageous approach to implementing an agile data environment, primarily because of its focus on "schema-on-read", thereby skipping the laborious, time-consuming, and fragile process of database modeling, refactoring, and re-indexing every time a new data set is ingested.
Another huge advantage of the data lake approach is the ability to annotate data sets and data granules with intelligent, searchable, reusable, flexible, user-generated, semantic, and contextual metatags. This tag layer makes your data "smart" -- and that makes your agile big data environment smart also!
The data contained in the data lake is too valuable to restrict its use to just data scientists. It would make the investment in a data lake more worthwhile if the target audience can be enlarged without hindering the original users. However, this is not the case today, most data lakes are single-purpose. Also, the physical nature of data lakes have potential disadvantages and limitations weakening the benefits and possibly even killing a data lake project entirely.
A multi-purpose data lake allows a broader and greater use of the data lake investment without minimizing the potential value for data science or for making it a less flexible environment. Multi-purpose data lakes are data delivery environments architected to support a broad range of users, from traditional self-service BI users to sophisticated data scientists.
Attend this session to learn:
* The challenges of a physical data lake
* How to create an architecture that makes a physical data lake more flexible
* How to drive the adoption of the data lake by a larger audience
Building a data lake is easy. Architecting a successful data lake that is flexible enough to accept multiple data sources, volumes, and types all while being able to scale with your business is harder.
Do it wrong and you've created a data swamp. Do it right and you turn data into the most valuable asset in your business.
Join us and learn from Rajesh Nadipalli, Zaloni’s Director of Product Support and Professional Services, how to:
- Set your data lake up for success with the right architecture
- Build guard rails to ensure the accuracy of data in your lake with proper data governance
- Provide visibility into your lake with a robust data catalog (or tie in with your favorite BI tools)
Data storage, data compute. Data ingestion. Metadata management. Governance. Visibility. Privacy. Transparency. These are just a few of the considerations you must plan for when modernizing your data platform with a data lake. It can be overwhelming, especially if you try to stitch specialized point products together yourself. Data lake implementations can get out of scope and out of control quickly.
Why pull your hair out trying to do it yourself? An actionable data lake is within reach. Join us as Nikhil Goel, Zaloni’s Lead Architect in Product Management, discusses the benefits that a turnkey data lake solution can provide as your data grows to meet your organization. Some of the topics covered will be:
• Storage and compute layers for cloud and on-premises
• Managed ingestion
• Zone-based data architecture
• Self-service access to the data catalog
• Customer success stories
This 1-hour webinar from GigaOm Research brings together leading minds in cloud data analytics, featuring GigaOm analyst Andrew Brust, joined by guests from cloud big data platform pioneer Qubole and cloud data warehouse juggernaut Snowflake Computing. The roundtable discussion will focus on enabling Enterprise ML and AI by bringing together data from different platforms, with efficiency and common sense.
In this 1-hour webinar, you will discover:
- How the elasticity and storage economics of the cloud have made AI, ML and data analytics on high-volume data feasible, using a variety of technologies.
- That the key to success in this new world of analytics is integrating platforms, so they can work together and share data
- How this enables building accurate, business-critical machine leaning models and produces the data-driven insights that customers need and the industry has promised
- How to make the lake, the warehouse, ML and AI technologies and the cloud work together, technically and strategically.
Register now to join GigaOm Research, Qubole and Snowflake for this free expert webinar.
Achieving actionable insights from data is the goal of any organization. To help in this regard, data catalogs are being deployed to build an inventory of data assets that provides both business and IT users a way to discover, organize and describe enterprise data assets. This is a good first step that helps all types of users easily find relevant data to extract insights from.
Increasingly, end users want to take the next step in provisioning or procuring this data into a sandbox or analytics environment for further use. Attend this session to see how organizations are looking to build actionable data catalogs via a data marketplace, that allow self-service access to data without sacrificing data governance and security policies.
Learn how to provide governed access and visibility to the data lake while still staying on track and within budget. Join Scott Gidley, Zaloni’s Vice President of Product, as he discusses:
- Architecting your data lake to support next-gen data catalogs
- Rightsizing governance for self-service data
- Where a data catalog falls short and how to address
- Success use cases
As more and more organizations delve into the world of big data, they’re noticing that it’s not wise to dump data into a data lake without proper guardrails in place. Instead, companies need to architect and build their data lake with scalability, flexibility and governance in mind.
Based on hundreds of data lake implementations, Zaloni has built a reference architecture that has proven to be scalable and future-proof. This architecture is based on a zone approach through which data can live and travel throughout its lifecycle. This zone-based approach can greatly facilitate data governance and management, particularly if a data lake management platform, such as the Zaloni Data Platform, is in place.
How should these zones be defined within a data lake environment? What should happen to data within each of these zones? In this webinar, Raj Nadipalli, Director of Product Support and Professional Services at Zaloni, will answer these questions and address how to architect a data lake that is future-proof in the ever-changing big data ecosystem.
Today, big data is enabling the advanced analytics that companies have dreamed of for driving their business. And as forward-thinking companies take advantage of big data and advanced analytics to drive digital transformation initiatives, it is forcing the laggards to realize that they will have to do the same if they want to survive.
The generally accepted architectural model for harnessing big data is a data lake. But data lakes, if leveraged simply as cheap storage within which to dump data, will inevitably disappoint. As the saying goes, garbage in, garbage out. Data lakes present unique challenges that must be dealt with if that big data set is going to be turned into actionable information.
So what does it take to succeed with a data lake? Why do some organizations get real value out of big data, while others struggle?
In this webinar, Matt Aslett, Research Director of Data Platform and Analytics at 451 Research and Kelly Schupp, VP of Data-driven Marketing at Zaloni, will discuss ideal data lake use cases such as Customer 360 and IoT. They will also discuss Zaloni’s data lake maturity model with which the data-eager company can chart its ideal course and roadmap.
It is easy to talk about the "Data Lake” as the answer to all data storage problems. However, not all Data Lakes are the same, and it is important to choose the right architecture for your data and use cases.
In this webinar, we will explore different Data Lake architectures - logical, storage, analytical etc. - from the point of view of the big data architect and user. We’ll understand the benefits of each, with examples drawn from the real-world experience of Hitachi Vantara in industries like manufacturing and finance.
Attendees will learn not only how to choose the model that works best for them, but will also come away with a sound understanding of the potential for analytics and intelligent applications built on their Data Lake architecture.
Data Lakes sind zentrale Datenspeicher, in denen sämtliche Daten gespeichert werden, die von Data Scientiest benötigt werden. Man kann sagen, ein One-Stop-Shop für Data Scientiests.
Die Architektur eines traditionellen Data Lakes hat jedoch zwei gravierende Nachteile:
1) Big Data Projekte, die zu groß sind um sie in eine zentrale Umgebung zu kopieren
2) Die eingeschränkte Nutzung
Was ist die Lösung? Ein multi-funktionaler Data Lake!
Was Sie in diesem Webinar lernen:
•Was Sie vermeiden sollten um das Potenzial Ihres Data Lake nicht zu schwächen
•Welche Funktionalitäten der Data Lake haben sollte, um Erkenntnisse daraus zu gewinnen
•Warum ein multifunktional genutzter Data Lake für ein universelles Data Delivery Systems unerlässlich ist
•Wie Sie einen logischen Mehrzweck-Data Lake mit Datenvirtualisierung aufbauen
End users use data lake from the cloud and go about doing their day-to-day operations very similar to how they would have for a business application hosted within an enterprise's own data center. Data Lake hosted in cloud With the various options provided by cloud providers, such as SaaS, PaaS, IaaS, and so on, there are ways in which business applications can be hosted in cloud, taking care of all the essential enterprise policies and governance. Because of this, many enterprises have chosen this as a way to host internally developed applications in these cloud providers. And definitely it is different and brings more value to big data pipeline.Read more >
The modern, data-rich enterprise demands access to data at a pace that has outclassed traditional data management platforms. Whether they are utilizing a cloud, hybrid, or on-prem solution, these organizations require capabilities that are vendor-neutral and often implemented with microservices to ensure an agile environment at scale.
In this webinar, Scott Gidley, Zaloni’s Vice President of Product, will showcase the latest version of the Zaloni Data Platform. This version provides exciting new features to address the growing demands of data-driven companies, including:
- Managing hybrid and multi-cloud environments
- Managing your data with zones
- Cloud-native support
- Ingestion wizard
- Platform global search
- Persona-driven homepage
Is Your Data Ready for GDPR?
As the deadline for GDPR approaches, it is time to get practical about protecting personal data.
We break down the steps for turning a data lake into a data hub with appropriate data management and governance activities: from capturing and reconciling personal data to providing for consent management, data anomyzation, and the rights of the data subject.
A smart approach to GDPR compliance lays a foundation for personalized and profitable customer and employee relations.
Watch, as experts from MAPR and Talend show you how to:
- Diagnose the maturity of your GDPR compliance;
- Set up milestones and priorities to reach compliance;
- Create a foundation to manage personal data through a data lake;
- Master compliance operations - from data inventory to data transfers to individual rights management.
For many companies, their data lake has either dried up, or it’s spilling over. And only a small percentage of businesses can claim victory in managing, analyzing and operationalizing datasets that exist as well as new sources.
Data-driven strategies help organizations better compete in the digital economy, giving them an advantage due to a more responsive business process. In this live discussion, the presenters will share the benefits of a “catalog-first strategy” which delivers a truly functional data marketplace or “data bazaar” as coined by 451 Research. Dr. Ring and Dr. Barth will show attendees the thinking behind the strategy, and how it represents a powerful enabler fueling the adoption of the self-service marketplace, including;
•Data as a Service; the components of a data bazaar/data marketplace
•From data sharing to data monetization
•The movement to cloud
•The Catalog-First strategy; Automated & metadata driven
•Building a Smart Catalog (smart data ingestion – validation/profiling)
•Making data business ready (cleansing, conformed, protected)
•Provisioning for Easy Consumption (browse/search, refresh, publish, access controls)
The idea of the data lake was alluring; the means to having data at your fingertips, filtered, profiled, secure and business-ready for data consumers to rapidly derive higher-levels of business value. However, this widely adopted concept didn’t come with an instruction manual.
The past few years have been turbulent times for enterprise data and analytics. In this live discussion, the presenters look beyond the lake, discussing the combination of self-service data preparation and data management and governance as one; a truly functional data marketplace or “data bazaar”. In addition, they will touch on other key enablers fueling the adoption of the self-service marketplace, including;
•Data as a service,
•Smart data ingestion – validation/profiling,
•Smart data cataloging/search,
As new data sources continue to emerge, companies need to create “golden” or master records to achieve a single version of truth, as well as enriched views of customer or product data for applications such as intelligent pricing, personalized marketing, smart alerts, customized recommendations, and more.
By leveraging machine learning techniques in the data lake, you can integrate data silos and master your data for a fraction of the cost of a traditional master data management solution. Zaloni’s Data Master Extension uses a Spark-based machine learning engine to provide a unique solution for Customer or Product 360° initiatives at the scale of big data.
In this webinar, Scott Gidley, Zaloni’s Vice President of Product, will lead the discussion around:
- Using a machine learning approach for matching and linking records
- Implementing master data management natively in the data lake
- A practical example of master data in the data lake
As data is growing at an exponential rate, organizations are increasingly looking to leverage streaming data from mobile devices, wearable technology and sensors for real-time processing and analytics. Gartner estimates that, “By 2020, 70% of organizations will adopt data streaming to enable real-time analytics.” However, implementing real-time data ingest, processing and delivering insights at scale requires infrastructure with zero latency and easy access to information when it is required.
In the webinar, we’ll discuss:
- Adopting Modern Data Lake with the Hortonworks Data Platform (HDP)
- Accelerating real-time data analytics with Hortonworks Data Flow (HDFTM) and Attunity to build a data lake
- Solving challenges with real-time data ingest and managing data in motion workloads
Join subject matter experts from IBM and Hortonworks for a joint webcast to help you accelerate real-time data analytics and manage your data workloads efficiently.
The concept of Data lakes evolved to address challenges and opportunities in managing big data.
Organizations are investing massive amounts of time and money to upgrade existing data infrastructures and build data lakes whether on-premises or in the cloud.
This talk will discuss architectures and design options to implement data lakes with open source tools. Also covered are challenges of upgrade & migration from existing data warehouses, metadata management, supporting self-service and managing production deployments.