Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes.

The Differences Between Data Warehouses and Data Lakes


The amount of data being generated and stored every day has exploded. Companies of all kinds are sitting on stockpiles of data that could someday prove valuable. Instead, businesses are increasingly turning to data lakes to store massive amounts of unstructured data.


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Data Lakes on Cloud & it’s Usage in Healthcare


Data lakes are centralized repositories that can store all structured and unstructured data at any desired scale. Data can be stored as-is, without first structuring it, and different types of analytics can be run on it, from dashboards and visualizations to big data processing, real-time analytics, and machine learning to improve decision making. The power of the data lake lies in the fact that it often is a cost-effective way to store data.

Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

Consultants and developers familiar with the AX data model could query the database using any number of different tools, including a myriad of different report writers. Data Entities. Currently, over 1,700 data entities are available and counting. The Data Warehouse Approach.

Complexity Drives Costs: A Look Inside BYOD and Azure Data Lakes

Jet Global

The Data Security Problem: How We Got Here. You can extract data from relational databases, including Microsoft’s SQL Server using the SQL query language. In addition to reading data, however, you can also use the SQL language to insert, update, or delete records from a database.

Data Cataloging in the Data Lake: Alation + Kylo


We are living in a new era of data defined by two massively disruptive trends – one architectural and the other organizational. Architecturally the introduction of Hadoop, a file system designed to store massive amounts of data, radically affected the cost model of data.

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. It will give insight on their advantages, differences and upon the testing principles involved in each of these data modeling methodologies. Let us begin with data warehouse. What is Data Warehouse? Let’s now move on to data lake.

A Look at Data Entities and BYOD for Accountants

Jet Global

Financial and operational reports retrieve master data and transactional information from your ERP databases using something called “SQL.” That stands for “structured query language.” (Don’t We refer to the first as “data entities.” Introducing Data Lakes.

2020 Data Impact Award Winner Spotlight: Merck KGaA


The Data Security and Governance category, at the annual Data Impact Awards, has never been so important. Finally, throw in the constant stream of cyberthreats out there and it’s clear that protecting your enterprise’s data is vital.

How to Avoid the 10 Big Data Analytics Blunders


Leading organizations are leveraging an analytics-driven approach—fueled and informed by data—to achieve marketplace advantages and create entirely new business models. Blunder #3: Not solving your real data science problem: dirty data.

Research quality data and research quality databases

Simply Statistics

When you are doing data science, you are doing research. You want to use data to answer a question, identify a new pattern, improve a current product, or come up with a new product. The common factor underlying each of these tasks is that you want to use the data to answer a question that you haven’t answered before. That is why the key word in data science is not data, it is science. The data is the substrate you use to get the answers you care about.

Building a Future for Life Sciences Data


I get the same kind of rush daily as lead data-ops engineer for Life Sciences at Tamr.* While the people, processes and tools involved in data pipelining obviously differ, the building method and life-changing results are similar. Modern data pipelines help turn the raw materials of life ( clinical research data ) into analytic outcomes that, in turn, speed the availability of new life-enhancing and -saving therapies. Ditto for legacy data.

Mastering Data Variety


Data variety — the middle child of the three Vs of Big Data — is in big trouble. . It’s in the critical path of enterprise data becoming an asset. And it’s been slow to benefit from the kind of technology advancements experienced by its “easier” siblings, data volume and data velocity. Meanwhile, most enterprises have unconsciously built up extreme data variety over the last 50+ years. Structured data” may sound like it’s organized.

What is Big Data Analytics?

Mixpanel on Data

Companies use big data analytics to uncover new and exciting insights in large and varied datasets. It helps them forecast market trends, identify hidden correlations between data flows, and understand their customers’ preferences in fine detail. But big data requires special infrastructure and a respect for the data science process. Big data analytics isn’t a synonym for data science, though the two are often confused. Big data also moves fast.

Approaches to Embrace Big Data

Perficient Data & Analytics

Not every organization starts its big data journey from the same place. However, in order to drive efficiencies, support expected future growth and to continue its evolution to a data-driven company, most organizations are reviewing their current suite of software solutions, platforms and documenting processes and areas of improvement along with devising and executing a strategy to deploy modern business intelligence capabilities. Baby Steps from EDW to Big Data.

Doing a 180 on Customer 360 – The Preferred Path to Customer Insights


The abundant growth of data, maturation of machine algorithms, and future regulatory compliance demands from the European Union’s General Data Protection Regulation (GDPR) will shift the landscape for creating a single source of the truth for customer data. Structured data from operational data stores now provides a small slice of the overall data needed to improve customer experience.

Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity


Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. The last 10+ years or so have seen Insurance become as data-driven as any vertical industry. Life insurance needs accurate data on consumer health, age and other metrics of risk. Why should Chief Data & Analytics Officers care about data security?

Big Data Fabric Weaves Together Automation, Scalability, and Intelligence


Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge. In conjunction with the evolving data ecosystem are demands by business for reliable, trustworthy, up-to-date data to enable real-time actionable insights.

Data Visualization and Visual Analytics: Seeing the World of Data


Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways.

Quantitative and Qualitative Data: A Vital Combination


Digging into quantitative data Why is quantitative data important What are the problems with quantitative data Exploring qualitative data Qualitative data benefits Getting the most from qualitative data Better together. Digging into quantitative data.

The Data Journey: From Raw Data to Insights


We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways organizations tackle the challenges of this new world to help their companies and their customers thrive.

Building Better Data Models to Unlock Next-Level Intelligence


You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. What is data modeling?

Using Artificial Intelligence to Make Sense of IoT Data


IoT is basically an exchange of data or information in a connected or interconnected environment. As IoT devices generate large volumes of data, AI is functionally necessary to make sense of this data. Data is only useful when it is actionable for which it needs to be supplemented with context and creativity. Traditional methods of analyzing structured data are not designed to efficiently process these large amounts of real-time data that is collected from IoT devices.

IoT 50

Create a Value Blizzard with Snowflake and Microsoft Azure

Sirius Computer Solutions

There are many benefits of using a cloud-based data warehouse, and the market for cloud-based data warehouses is growing as organizations realize the value of making the switch from an on-premises data warehouse.

Business Analytics with AI, ML and Blockchain

Perficient Data & Analytics

Artificial Intelligence is already being used to conextualize data and significantly reduce efforts to transform data sets for better insights. Adaptive intelligence is being infused into cloud applications by vendors like Oracle to drive data-driven intelligence based on Machine Learning algorithms at vastly increased Speeds. Each can be a source of data as well as a mechanism to improve the analytics you are utilizing for your organization.

Why You Need a Data Catalog & How to Choose One


If the point of Business Intelligence (BI) data governance is to leverage your datasets to support information transparency and decision-making, then it’s fair to say that the data catalog is key for your BI strategy. At least, as far as data analysis is concerned. The right data catalog tool can be a powerful complement to your existing BI processes. The Benefits of Structured Data Catalogs. Choosing a Data Catalog. Smart Data Catalog Tools.

In-depth with CDO Christopher Bannocks

Peter James Thomas

Today I am talking to Christopher Bannocks , who is Group Chief Data Officer at ING. As stressed in other recent In-depth interviews [1] , data is a critical asset in banking and related activities, so Christopher’s role is a pivotal one. Hello Christopher, can you start by providing readers with a flavour of your career to date and perhaps also explain why you came to focus on the data arena. 2] I was asked to help solve the data problem.