#data lakes vs data warehouse vs data mart

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A Beginner’s Guide to Data Warehousing

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A Beginner’s Guide to Data Warehousing

In this digital economy, data is paramount. Today, all sectors, from private enterprises to public entities, use big data to make critical business decisions.

However, the data ecosystem faces numerous challenges regarding large data volume, variety, and velocity. Businesses must employ certain techniques to organize, manage, and analyze this data.

Enter data warehousing! 

Data warehousing is a critical component in the data ecosystem of a modern enterprise. It can streamline an organization’s data flow and enhance its decision-making capabilities. This is also evident in the global data warehousing market growth, which is expected to reach $51.18 billion by 2028, compared to $21.18 billion in 2019.

This article will explore data warehousing, its architecture types, key components, benefits, and challenges.

What is Data Warehousing?

Data warehousing is a data management system to support Business Intelligence (BI) operations. It is a process of collecting, cleaning, and transforming data from diverse sources and storing it in a centralized repository. It can handle vast amounts of data and facilitate complex queries.

In BI systems, data warehousing first converts disparate raw data into clean, organized, and integrated data, which is then used to extract actionable insights to facilitate analysis, reporting, and data-informed decision-making.

Moreover, modern data warehousing pipelines are suitable for growth forecasting and predictive analysis using artificial intelligence (AI) and machine learning (ML) techniques. Cloud data warehousing further amplifies these capabilities offering greater scalability and accessibility, making the entire data management process even more flexible.

Before we discuss different data warehouse architectures, let’s look at the major components that constitute a data warehouse.

Key Components of Data Warehousing

Data warehousing comprises several components working together to manage data efficiently. The following elements serve as a backbone for a functional data warehouse.

Data Sources: Data sources provide information and context to a data warehouse. They can contain structured, unstructured, or semi-structured data. These can include structured databases, log files, CSV files, transaction tables, third-party business tools, sensor data, etc.

ETL (Extract, Transform, Load) Pipeline: It is a data integration mechanism responsible for extracting data from data sources, transforming it into a suitable format, and loading it into the data destination like a data warehouse. The pipeline ensures correct, complete, and consistent data.

Metadata: Metadata is data about the data. It provides structural information and a comprehensive view of the warehouse data. Metadata is essential for governance and effective data management.

Data Access: It refers to the methods data teams use to access the data in the data warehouse, e.g., SQL queries, reporting tools, analytics tools, etc.

Data Destination: These are physical storage spaces for data, such as a data warehouse, data lake, or data mart.

Typically, these components are standard across data warehouse types. Let’s briefly discuss how the architecture of a traditional data warehouse differs from a cloud-based data warehouse.

Architecture: Traditional Data Warehouse vs Active-Cloud Data Warehouse

A Typical Data Warehouse Architecture

Traditional data warehouses focus on storing, processing, and presenting data in structured tiers. They are typically deployed in an on-premise setting where the relevant organization manages the hardware infrastructure like servers, drives, and memory.

On the other hand, active-cloud warehouses emphasize continuous data updates and real-time processing by leveraging cloud platforms like Snowflake, AWS, and Azure. Their architectures also differ based on their applications.

Some key differences are discussed below.

Traditional Data Warehouse Architecture

Bottom Tier (Database Server): This tier is responsible for storing (a process known as data ingestion) and retrieving data. The data ecosystem is connected to company-defined data sources that can ingest historical data after a specified period.

Middle Tier (Application Server): This tier processes user queries and transforms data (a process known as data integration) using Online Analytical Processing (OLAP) tools. Data is typically stored in a data warehouse.

Top Tier (Interface Layer): The top tier serves as the front-end layer for user interaction. It supports actions like querying, reporting, and visualization. Typical tasks include market research, customer analysis, financial reporting, etc.

Active-Cloud Data Warehouse Architecture

Bottom Tier (Database Server): Besides storing data, this tier provides continuous data updates for real-time data processing, meaning that data latency is very low from source to destination. The data ecosystem uses pre-built connectors or integrations to fetch real-time data from numerous sources.

Middle Tier (Application Server): Immediate data transformation occurs in this tier. It is done using OLAP tools. Data is typically stored in an online data mart or data lakehouse.

Top Tier (Interface Layer): This tier enables user interactions, predictive analytics, and real-time reporting. Typical tasks include fraud detection, risk management, supply chain optimization, etc.

Best Practices in Data Warehousing

While designing data warehouses, the data teams must follow these best practices to increase the success of their data pipelines.

Self-Service Analytics: Properly label and structure data elements to keep track of traceability – the ability to track the entire data warehouse lifecycle. It enables self-service analytics that empowers business analysts to generate reports with nominal support from the data team.

Data Governance: Set robust internal policies to govern the use of organizational data across different teams and departments.

Data Security: Monitor the data warehouse security regularly. Apply industry-grade encryption to protect your data pipelines and comply with privacy standards like GDPR, CCPA, and HIPAA.

Scalability and Performance: Streamline processes to improve operational efficiency while saving time and cost. Optimize the warehouse infrastructure and make it robust enough to manage any load.

Agile Development: Follow an agile development methodology to incorporate changes to the data warehouse ecosystem. Start small and expand your warehouse in iterations.

Benefits of Data Warehousing

Some key data warehouse benefits for organizations include:

Improved Data Quality: A data warehouse provides better quality by gathering data from various sources into a centralized storage after cleansing and standardizing.

Cost Reduction: A data warehouse reduces operational costs by integrating data sources into a single repository, thus saving data storage space and separate infrastructure costs.

Improved Decision Making: A data warehouse supports BI functions like data mining, visualization, and reporting. It also supports advanced functions like AI-based predictive analytics for data-driven decisions about marketing campaigns, supply chains, etc.

Challenges of Data Warehousing

Some of the most notable challenges that occur while constructing a data warehouse are as follows:

Data Security: A data warehouse contains sensitive information, making it vulnerable to cyber-attacks.

Large Data Volumes: Managing and processing big data is complex. Achieving low latency throughout the data pipeline is a significant challenge.

Alignment with Business Requirements: Every organization has different data needs. Hence, there is no one-size-fits-all data warehouse solution. Organizations must align their warehouse design with their business needs to reduce the chances of failure.

To read more content related to data, artificial intelligence, and machine learning, visit Unite AI.

[garymdm]

Data Lake vs Data Cesspool

Data Lake vs Data Cesspool - The value of #DataGovernance for #BigData

Visual Linage and Context for Hadoop analytics and integration Andrew C. Oliver’s (@acoliver) recent post “How to create a data lake for fun and profit”  is an interesting take on the value of a data lake – an unstructured data warehouse where you pull in all your different sources into one large “pool” of data.  Schema-on-Read In contrast to data marts and warehouses, a data lake doesn’t…

[analyticssteps]

A data lake is a consolidated repository for accumulating all the structured and unstructured data at a large scale or small scale. Talking about buzzwords today regarding data management, and listing here is Data Lakes, and Data Warehouse, what are they, why and where to deploy them. So, in this blog, we will unpack their definition, key differences, and what we see in the near future.  

[rajaniesh]

What is Databricks Lakehouse and why you should care

What is Databricks Lakehouse and why you should care

In recent times, Databricks has created lots of buzz in the industry. Databricks lays out the strong foundation of Data engineering, AI & ML, and streaming capabilities under one umbrella. Databricks Lakehouse is essential for a large enterprise that wants to simplify the data estate without vendor lock-in. In this blog, we will learn what Databricks lakehouse is and why it is important to…

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How Data virtualization Helps Organizations Succeed

Every business organization wants to opt for well-governed and consistent data that is easy to use and access. It provides the prerequisite opportunity to the business enterprise to explore the data for insights successfully and easily. It offers data as a service, and reports in real-time, control the different digital operations. You should remember that business enterprises adopt myriad different strategies for putting the data house in order. It is inclusive of the data marts, data warehouses, ETL, big data, and different cloud data lakes.

However, older techniques are not sufficient to confer the accessibility and agility, as required by the digital businesses. Data virtualization solutions is worth mentioning as it helps in resolving the challenge. As you try to use data virtualization, you will generate the modern data integration layer that allows you to deliver the data within the business-relevant option.

Hence, you can make the best use of the latest data of different business users from various distributed sources of data. Besides this, you can free the users of the business to apply the data from a variety of applications and analytics. As you go through this article, you will be capable of understanding how Data virtualization will be useful to a business organization in getting success:

Data virtualization helps in saving money.

The data virtualization platforms provide ample opportunity. It is not going to resolve the issue of data harvesting. It is another issue which helps in saving an ample amount of money. Such types of information are useful to the business organization in keeping extra money within an exclusive budget. But, you should think of different ways in which you will hemorrhage the money, owing to the fragmented ways in which you will store the data.

There are a plethora of employees who might give up before the execution of different comprehensive searches. On the other hand, a few of them might make use of different substandard workarounds. In addition to this, there are people capable of patching different kinds of protocol, who will release the complete search. It is going to waste an ample amount of time and money at the same time. Data virtualization is worth mentioning in this regard as it helps in resolving the unnecessary and frustrating issues of the past.

Using better analytics

It is not the best option to think of the different types of searches. It is not possible for such kind of software to pull it off. In place of this, you should consider the analytics, which are made available to you. Better analytics is derived from the compilation of the data at the starting of the process. Every time information gets created in the company; you should ensure that it is marked immediately for the business enterprise. It is accomplished by such kind of software as you require performing the analytics for specific reasons. 

Organization of the latest data

Do you think, what you should do about the kind of data so that the analytics will get its hold? As you integrate it virtually, you are going to have it in a single place. Previously, it was challenging for business enterprises to do it. Business organizations make the right use of the Extract, Transform and Load or ETL technique to accomplish this. As you are doing this, you are sure to know about the shortcomings. The primary one is the transformation of the extracted data and loading the same.

Choosing the correct platform

In case you are selling on virtualization, the next step involves investing in the platform. While there are a plethora of options involved, you can be ensured that it is easier said. All you need to do is look at different kinds of consumers and providers, as far as the data is concerned. You should be capable of integrating different DB2 applications and confer real-time performance.

According to Gartner, the majority of the big projects encounter failure, whereas 70 percent of the projects related to Big Data are not that profitable. So, it can be said that big data projects can be challenging. No single reason is responsible for such kind of situation. Also, it is complicated to use different processing technologies and big data storage. The technology seems to be new for the majority of IT specialists. Big data projects are beneficial for the wrong use cases. In spite of such disappointing results, business enterprises are known to initiate big data projects to reap the business's potential benefits.  

Data virtualization is useful in the simplification of big data projects. Though it will not resolve different issues, you can be ensured that it is deployed for the different right use cases. So, it enhances the chances for the success of big data projects. A huge data amount gets stored within the plain files. Owing to this, it becomes challenging for different non-tech-savvy users of the business to get access to it.

You should remember that different data virtualization servers are capable of hiding such complexity. So, the audience of a larger business can ensure data availability to different BI tools. They make the right use of the virtual table for the encapsulation of big data. In business enterprises, it is possible that big data is generated across the world remotely.

Data is produced in different manufacturing plants, factories, and stores. The total amount of data, which is generated by every remote site, is excessive so that it is copied to the central location for different analytical and reporting objectives.

Summary

Data virtualization has become an integral part of business enterprises as it is useful in streamlining different challenges, which affect the management of organizational data for several years. Data virtualization helps in establishing the guidelines, which state that the data virtualization platform is useful to seek access to different data vs. traditional techniques.

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New Post has been published on https://techcrunchapp.com/tech-mn-faq-friday-data-lakes/

tech.mn – FAQ Friday — Data Lakes

Welcome to our latest FAQ Friday — data lakes FAQ — where industry experts answer your burning technology and startup questions. We’ve gathered top Minnesota authorities on topics from software development to accounting to talent acquisition and everything in between. Check in each week, and submit your questions here.

This week’s FAQ Friday is sponsored by Coherent Solutions. Coherent Solutions is a software product development and consulting company that solves customer business problems by bringing together global expertise, innovation, and creativity. The business helps companies tap into the technology expertise and operational efficiencies made possible by their global delivery model.

Meet Our FAQ Expert

Max Belov, CTO of Coherent Solutions

Max Belov

Max Belov has been with Coherent Solutions since 1998 and became CTO in 2001. He is an accomplished architect and an expert in distributed systems design and implementation. He’s responsible for guiding the strategic direction of the company’s technology services, which include custom software development, data services, DevOps & cloud, quality assurance, and Salesforce.

Max also heads innovation initiatives within Coherent’s R&D lab to develop emerging technology solutions. These initiatives provide customers with top notch technology solutions IoT, blockchain, and AI, among others. Find out more about these solutions and view client videos on the Coherent Solutions YouTube channel.

Max holds a master’s degree in Theoretical Computer Science from Moscow State University. When he isn’t working, he enjoys spending time with his family, on a racetrack, and playing competitive team handball.

Let’s start simple — What are data lakes? What is data warehouse?

Data lakes are centralized data repositories that are capable of securely storing large amounts of data in a variety of its native formats. It allows to consumers to search for relevant data in the repository and query it by defining the structure that makes sense at the time of use. In simple terms, we don’t really care what format data has when we capture and store it. Format only becomes relevant when we start to analyze the data and can therefore use the same source data for new types of analysis as the need arises. There are a variety of tools and techniques one can use to implement efficient data lakes.

A data warehouse is similar to data lakes in that it is also capable of storing and making available for analysis large volumes of data. However, this is where similarities end. A data warehouse typically has stricter data architecture and design that needs to be defined before you start populating it with data. A data warehouse uses relational representation of your data and the data in the repository needs to be structured according to how you are planning to use it in the future. While feeding data from your data warehouse into purpose-built data marts may add flexibility to the solution, a significant re-architecture effort may be required to add additional data types to a data warehouse or to support new types of data analysis.

A data warehouse can be used to complement a data lake. You would land data in a data lake, perform initial analysis, and then send the data to a data warehouse designed for a certain business or data domain.

Here is an easy comparison between data lake and data warehouse.

  Data Warehouse Data Lake Data Regional Data Structure, Semi-structured, Unstructured data Data Quality Highly curated data, source of truth Raw data Schema Most often designed prior to implementation (Schema-on-write) Defined at the time of analysis (schema-on-read) Users Business users, data developers Data scientists, data developers, data engineers, data architects Usage Reporting, Business Intelligence, Visualization Exploratory Analysis, Discovery, Machine Learning, Profiling

How do you know if your business is right for data lake or data warehouse, and how can it be a benefit?

A data lake architecture enables organizations to handle ever increasing data volumes, varieties, and velocities, while continuing to provide security, ability to consistently process and govern the data. A single repository can then service many different types of analytics workloads such as visualizations, dashboards, and machine learning.

A data lake enables the business to introduce additional use cases for the data without impacting existing ones. It also provides separation between storage and compute thus ensuring that different applications that consume the data will have minimal impact on each other.

So, what does a successful implementation of a data lake looks like?

There are five key pillars of a successful data lake solution:

Data Ingestion/Processing Mechanism: Proper selection allows you to properly support expected volume of the data and its velocity (how much data you have to begin with and how quickly the new data is coming in).

Data Catalog: This is what keeps your data lake a lake, not a swamp. It provides the metadata describing the content of your data lake — the meaning of various data within it.

Data Storage: Your data lake is not a single centralized storage bucket. There’s a logical and physical structure that helps you break data by your processing lifecycle (raw vs cleansed), by the type of a source system it is coming from, by how you intend to use it (since its final data format and representation may vary), and also depending on the analysis you are trying to perform, etc.

Data Lake Governance and Platform Services: This is the glue that holds everything together starting from infrastructure management (provisioning, monitoring, scheduling), data quality (ensuring provided data is a reliable fit for the intended purpose within the enterprise) and data lineage (understanding where the data is coming from to data security, how it changes/evolves as it moves from the source into the data lake and through data lake) to data security (controlling data access and preventing breaches through implementing appropriate network and access control mechanisms, and end-to-end encryption of the data).

Data Exploration and Visualization: You should define which groups within the company are going to be the consumers of data from the data lake and carefully examine their real (not just declared) needs, consumption scenarios, analytical proficiency and currently used toolset. Proper selection of Data Exploration and Visualization component is the key to user adoption and therefore the success of the overall endeavor.

From the implementation perspective, there are some key decisions that need to be made.

Where you are going to host your data? This can be within your own data center or within one of the public cloud providers. The actual technical solution you develop may be portable across different providers, but once you deploy it and start accumulating data, you will be locked in since migrating large amounts of data from one platform to another may prove to be a very expensive proposition if you decide to change providers.

What data storage technology will you use? Choosing optimal solutions will help you balance durability and scale data access throughput, security (access audit, encryption at rest and transit), and cost efficiency.

Hungry for more information on Data Services? Visit Coherent Solutions.

Still curious? Ask Max and the Coherent Solutions team questions on Twitter at @CoherentTweets.

Don’t stop learning! Get the scoop on a ton of valuable topics from Max Belov and Coherent Solutions in our FAQ Friday archive.

FAQ Friday — Digital Apps

FAQ Friday — eCommerce

FAQ Friday – Security and Working Remotely

FAQ Friday – Machine Learning