The Data Lake is Dead; Long Live the Data Lake!

Teradata

Martin Wilcox examines the failure of data lakes

Here’s Why Automation For Data Lakes Could Be Important

Smart Data Collective

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation. There were a lot of promises made about Big Data that fell at the feet of data scientists to make happen. Big Data is, well…big.

Data Lakes on Cloud & it’s Usage in Healthcare

BizAcuity

Data lakes are centralized repositories that can store all structured and unstructured data at any desired scale. The power of the data lake lies in the fact that it often is a cost-effective way to store data. Deploying Data Lakes in the cloud.

Data Lakes: What Are They and Who Needs Them?

Jet Global

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. To address the flood of data and the needs of enterprise businesses to store, sort, and analyze that data, a new storage solution has evolved: the data lake.

Data Lake Consolidation – the Aggregator Analogy

Perficient Data & Analytics

In my last post I introduced the concept of the Data Lake as a Consolidator and the critical success factor of applying robust Information Governance to this environment. So, a Data Lake as Consolidator. Data & Analytics Healthcare data governance data lake data lake

Hadoop and data lakes require further examination

BI-Survey

A fter the hype comes disillusionment and the growing realization that Hadoop and data lakes do not provide the answer for all analytic tasks. Hadoop and Data Lakes Report. Request the free report now × Hadoop and Data Lakes.

Working with the Data Lake Aggregator – Standards and Templates

Perficient Data & Analytics

In my previous blog , I described the concept of an “Information Catalog” and how it plays a vital role in ensuring communication between the Data Lake Aggregator and Suppliers and Consumers is efficient and effective due to the common language that it provides.

Power BI + Azure Data Lake = Velocity & Scale to Your Analytics

Perficient Data & Analytics

Context – Bring data together from various web, cloud and on-premise data sources and rapidly drive insights. The biggest challenge Business Analysts and BI developers have is the need to ingest and process medium to large data sets on a regular basis. Common Data Model.

Unlocking the Potential of Machine Learning in a Data Lake

Data Virtualization

With data becoming the brain food to the intelligence of every organization, regardless of size or sector, it has become crucial to harness this data to achieve the best results, make the most informed decisions and improve productivity. Technology artificial intelligence big data Data integration Data Lake data virtualization Logical Data Lake Machine learning

Data Lake Participants – Roles and Responsibilities

Perficient Data & Analytics

As you may recall, last time I introduced the analogy of the Aggregator to describe utilizing a Data Lake as a Consolidator of information, and I mentioned the three key roles in this model: the Supplier, the Aggregator and the Consumer. In this post I will provide a little more detail on the responsibilities possessed by each of these roles that, when carried out diligently, provide an effective environment for obtaining significant value from the Lake.

Data Lake as Aggregator – The Critical Role of the Catalog

Perficient Data & Analytics

My previous blog talked about a Data Lake using a Supplier-Aggregator-Consumer analogy and talking about the roles each of these parties play.

Data Lake and Information Governance – The Key Takeaways

Perficient Data & Analytics

A Data Lake can be a highly valuable asset to any enterprise, and there is a myriad of technology solutions available for leveraging the processes to feed, maintain and retrieve information from the Lake. This is the primary Takeaway to keep in mind when a Data Lake solution is being considered – or is already in place but needing improvement – by any organization. So, this completes my journey into Data Lakes and the Information Governance needed.

Alternative approaches to implementing your data lake

ScienceSoft

ScienceSoft answers burning questions about big data lake design and implementation. We look at different approaches to its architecture and contemplate if there exists a preferred technology among the available stack

What's the difference between data lakes and data warehouses?

IBM Big Data Hub

If you’ve heard the debate among IT professionals about data lakes versus data warehouses, you might be wondering which is better for your organization. You might even be wondering how these two approaches are different at all

Data Lakes and the Information Governance Critical Success Factor

Perficient Data & Analytics

Since my last post I’ve been working for a client that is actively engaged in establishing a Data Lake for the purpose of supporting their analytics efforts, but also looking to “re-architect” the way their systems collaborate by using this Data Lake environment to control and consolidate all information-sharing interactions within their environment. Data & Analytics Healthcare Big Data Governance data governance data lake data lakes

Data Lakes, Not Just For Analytics Anymore

Perficient Data & Analytics

Data Lakes have been around since the early part of this decade as most Fortune 500 companies have a Data Lake or are building a Data Lake. The drive to lake data has predominately been driven by analytical use cases where Data Scientists can wrangle and prepare data for their study or model building. In my next blog post, I will investigate these challenges that companies are facing as Big Data becomes operational.

Data Management on Display at Informatica World 2019

David Menninger's Analyst Perspectives

Under that focus, Informatica's conference emphasized capabilities across six areas (all strong areas for Informatica): data integration, data management, data quality & governance, Master Data Management (MDM), data cataloging, and data security.

Test principles – Data Warehouse vs Data Lake vs Data Vault

Perficient Data & Analytics

Understand Data Warehouse, Data Lake and Data Vault and their specific test principles. This blog tries to throw light on the terminologies data warehouse, data lake and data vault. Let us begin with data warehouse. What is Data Lake?

Providing transactional data to your Hadoop and Kafka data lake

IBM Big Data Hub

The data lake may be all about Apache Hadoop, but integrating operational data can be a challenge.

Data Management Requirements for the Enterprise Data Lake

In(tegrate) the Clouds

SnapLogic published Eight Data Management Requirements for the Enterprise Data Lake. They are: Storage and Data Formats. The company also recently hosted a webinar on Democratizing the Data Lake with Constellation Research and published 2 whitepapers from Mark Madsen. big data integration data lake hadoop snaplogicIngest and Delivery. Discovery and Preparation. Transformation and Analytics. Streaming. Scheduling and Workflow.

Get out of the data swamp with a governed data lake

IBM Big Data Hub

Making your data lake a “governed data lake” is the game changer. Without governance, organizations risk securing the data and as well as protecting it. A governed data lake contains data that’s accessible, clean, trusted and protected.

Cloudera announces support for Azure’s next-generation Data Lake Store

Cloudera

The Cloudera platform delivers a one-stop shop that allows you to store any kind of data, process and analyze it in many different ways in a single environment, and integrate with the rest of your data infrastructure. Before they can fully realize the benefits of the cloud, they have had to adjust to new data models and new processes. Eventual consistency and other pitfalls can be a nightmare for engineers trying to migrate complex big data infrastructure to the cloud.

Big Data for Business: A Requirement for Today’s Business Analytics

David Menninger's Analyst Perspectives

Organizations now must store, process and use data of significantly greater volume and variety than in the past. Analytics Business Intelligence Data Governance Data Preparation Information Management Internet of Things Data Digital Technology blockchain data lakes AI and Machine Learning

The Internet of Things: Real-Time Data and Analytics Enable Business Innovation

David Menninger's Analyst Perspectives

This innovation means that virtually any appropriately designed device can generate and transmit data about its operations, which can facilitate monitoring and a range of automatic functions.

The business value of a governed data lake

IBM Big Data Hub

Imagine a searchable data management system that would enable you to review crowdsourced, categorized and classified data. Consider that this system would apply to all types of data — structured and unstructured — and become more robust as more users analyze it

3 principles for climbing the AI ladder with IBM Governed Data Lake

IBM Big Data Hub

Recently, we capped off the first leg of the “Enabling digital business with an IBM governed data lake” road shows in the Asia Pacific region with our customers and partners

AmFam's Data Journey From Legacy To Cloud: Teaching People To Fish In The Data Lake

Bruno Aziza

AmFam’s journey from a data-rich company to a data-driven company

IDG Contributor Network: How to overcome the bottlenecks between data lakes and analytics for customer engagement

CIO Business Intelligence

Many organizations in a variety of industries struggle to access the customer data they need to provide personalized and contextual experiences across all touchpoints. Recently, data lakes have been touted as the best way to manage the variety of collected customer data, with many big data and analytics solutions focused on a self-service approach to leveraging the value of the data lake.

News and Announcements from Tableau and TC18

David Menninger's Analyst Perspectives

Once again I attended Tableau's Users Conference, along with 17,000 other attendees, affectionately self-referred to as "data nerds". Big Data Data Governance Data Integration Data Preparation Tableau Software data lakes

Meet Perficient’s Chief Strategists: Bill Busch

Perficient Data & Analytics

Big data has significantly impacted today’s leading enterprises “as it helps detect patterns, consumer trends, and enhance decision making.” In fact, the big data and analytics market is estimated to reach $49 billion this year with a CAGR of 11 percent.

More Structured or Less, Data Virtualization Delivers

Data Virtualization

Alice Well’s early successes as CIO at Advanced Banking Corporation (ABC) in solving the old problem of getting real-time data (Gaining Real Time Insight) to the call center and the newer opportunity presented by the data lake (A Warehouse in.

The Path to Artificial Intelligence in Healthcare

Perficient Data & Analytics

In an industry that has massive amounts of data and is very dependent on the data to both run efficiently and, more importantly, delivery high quality patient care any technology which can lead to significant improvement is anticipated. First of all, while you can input data from many different sources, the best approach, is to layer Artificial Intelligence applications on top of an established data infrastructure.

Information Governance – Essential Ingredient for Business Value

Perficient Data & Analytics

In my last blog, you may recall that we were discussing the value and the need for Standards and Templates for ensuring a consistent and efficient use of the Data Lake, both in its population (supplying) and in its retrieval (consuming) of information. As far as Rules, Decision Rights and Processes, we need to consider the overall purpose and role of a Data Lake and craft these accordingly. Decide what information will be resident in the Lake.

Constructing A Digital Transformation Strategy: Putting the Data in Digital Transformation

erwin

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there. Constructing A Digital Transformation Strategy: Data Enablement. With automation, data quality is systemically assured.

Simplifying Big Data Projects with Data Virtualization

Data Virtualization

According to Gartner, 60% of all the big data projects fail and according to Capgemini 70% of the big data projects are not profitable. There can only be one conclusion, big data projects are hard!

You Need a Better Data Management Solution

TDAN

Become a “Data-First” Company The organizations that invest in data first and foremost are changing the business landscape. Apple had famously reached a trillion dollar valuation on August 2, 2018, and analysts predicted that Amazon wasn’t far behind.

Data Virtualization: Thinking Outside the Bowl

Data Virtualization

Across all vertical markets, organizations adopt data virtualization as a core part of their IT infrastructure because data is becoming more distributed, more heterogeneous, and larger in volume.

Common Ingestion Framework

Perficient Data & Analytics

Big Data is the way to move forward for all enterprises today. May it be healthcare, retail, finance or manufacturing, everyone is at different stages in their journey to create their industry-grade, enterprise-ready Data Lake repository.

Cloud Trends in 2019: Unlocking Your Data

Perficient Data & Analytics

Businesses have a lot of data. However, the sheer amount of data that businesses have can even surprise them. This is because old models and legacy systems have often left data siloed and unable to interact with other data. Data being stored in these disparate places symbolizes the lack of a cohesive model that businesses can suffer from. This is because the cloud doesn’t just connect this data. Simply put, the cloud allows your data to be dynamic.

3 business realities fueling the need for enterprise data preparation

IBM Big Data Hub

The pressure is rising for business users, automation, and governed data lakes to drive business value. Learn how enterprise data preparation fits in