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The key Difference Between a Data Warehouse and Data lake

The key Difference Between a Data Warehouse and Data lake

Introduction

Enterprises have long relied on BI to help them move their businesses forward. Years ago, translating BI into actionable information required the help of data experts. Today, technology supports BI which is accessible to people at all levels of an enterprise.

All that BI data needs to live somewhere. The data storage solution you choose for enterprise app development positions your business to access, secure, and use data in different ways. That’s why it’s helpful to understand the basic options, how they’re different, and which use cases are suitable for each.

In this blog, we will be looking at the key differences between Data lakes and Data warehouses. We will understand their basics and will try to see their implementation in different fields with different tools.

What is Data Lake?

A data lake is a central location in which you can store all your data, regardless of its source or format. It is typically, although not always, built using Hadoop. The data can be structured or unstructured. You can then use a variety of storage and processing tools — typical tools in the extended Hadoop ecosystem — to extract value quickly and inform key organizational decisions. Because of the growing variety and volume of data, data lakes are an emerging and powerful architectural approach, especially as enterprises turn to mobile, cloud-based applications, and the Internet of Things (IoT) as right-time delivery mediums for big data.

What is a Data Warehouse?

A data warehouse is a large collection of business data used to help an organization make decisions. The concept of the data warehouse has existed since the 1980s when it was developed to help transition data from merely powering operations to fueling decision support systems that reveal business intelligence.

A large amount of data in data warehouses comes from different places such as internal applications such as marketing, sales, and finance; customer-facing apps; and external partner systems, among others. On a technical level, a data warehouse periodically pulls data from those apps and systems; then, the data goes through formatting and import processes to match the data already in the warehouse. The data warehouse stores this processed data so itʼs ready for decision-makers to access. How frequently data pulls occur, or how data is formatted, etc., will vary depending on the needs of the organization.

Differences

1. Data Types

Data warehouses store structured organizational data such as financial transactions, CRM and ERP data. Other data sources such as social media, web server logs, and sensor data, not to mention documents and rich media, are not storable because they are more difficult to model, and their sheer volume makes them expensive and difficult to manage. These types of data are more appropriate for a data lake.

2. Processing

In a data warehouse, data is organized, defined, and metadata is applied before the data is written and stored. We call this process as ‘schema on writeʼ. A data lake consumes everything, including data types considered inappropriate for a data warehouse. Data is present in raw form; information is present to the schema as we extract data from the data source, not when we write it to storage. We call this as a ‘schema on readʼ.

3. Storage and Data Retention

Before we can load data to a data warehouse, data engineers work hard to analyze the data and how to use it for business analysis. They design transformations to summarize and transform the data to enable the extraction of relevant insights. They do not consider the data which doesnʼt answer concrete business questions in the data warehouse. In order to reduce storage space and improve performance — a traditional data warehouse is an expensive and scarce enterprise resource. In a data lake, data retention is less complex, because it retains all data — raw, structured, and unstructured. Data is never going in the deletion phase, permitting analysis of past, current and future information. Data lakes run on commodity servers using inexpensive storage devices, removing storage limitations.

4. Agility

Data warehouses store historical data. Incoming data conforms to a predefined structure. This is useful for answering specific business questions, such as “what is our revenue and profitability across all 124 stores over the past week”. However, if business questions are evolving, or the business wants to retain all data to enable in-depth analysis, data warehouses are insufficient. The development effort to adapt the data warehouse and ETL process to new business questions is a huge burden. A data lake stores data in its original format, so it is immediately accessible for any type of analysis. Information can be retrieved and reused — a user can apply a formalized schema to the data, store it, and share it with others. If the information is not useful, the copy can be discarded without affecting the data stored in the data lake. All this is done with no development effort.

5. Security, Maturity, and Usage

Data warehouses have been around for two decades and are a secure, enterprise-ready technology. Data lakes are getting there, but are newer and have a shorter enterprise track record. A large enterprise cannot buy and implement a data lake like it would a data warehouse — it must consider which tools to use, open source or commercial, and how to piece them together to meet requirements. The end users of each technology are different: a data warehouse is used by business analysts, who query the data via pre-integrated reporting and BI. Business users cannot use a data lake as easily, because data requires processing and analysis to be useful. Data scientists, data engineers, or sophisticated business users, can extract insights from massive volumes of data in the data lake.

Benefits of Data lakes

1. The Historical Legacy Data Architecture Challenge

Some reasons why data lakes are more popular are historical. Traditional legacy data systems are not that open, to say the least, if you want to start integrating, adding and blending data together to analyze and act. Analytics with traditional data architectures weren’t that obvious nor cheap either (with the need for additional tools, depending on the software). Moreover, they weren’t built with all the new and emerging (external) data sources which we typically see in big data in mind.

2. Faster Big Data Analytics as a Driver of Data Lake Adoption

Another important reason to use data lakes is the fact that big data analytics can be done faster. In fact, data lakes are designed for big data analytics if you want and, more important than ever, for real-time actions based on real-time analytics. Data lakes are fit to leverage big quantities of data in a consistent way with algorithms to drive (real-time) analytics with fast data.

3. Mixing and Converging Data: Structured and Unstructured in One Data Lake

A benefit we more or less already mentioned is the possibility to acquire, blend, integrate and converge all types of data, regardless of sources and format. Hadoop, one of the data lake architectures, can also deal with structured data on top of the main chunk of data: the previously mentioned unstructured data coming from social data, logs and so forth. On a side note: unstructured data is the fastest growing form of all data (even if structured data keeps growing too) and is predicted to reach about 90 percent of all data.

Benefits of Data Warehousing

Organizations that use a data warehouse to assist their analytics and business intelligence to see a number of:

  1. Substantial Benefits
    Better data, hence adding data sources to a data warehouse enables organizations to ensure that they are collecting consistent and relevant data from that source. They donʼt need to wonder whether the data will be accessible or inconsistent as it comes into the system. This ensures higher data quality and data integrity for sound decision making.
  2. Faster Decisions
    Data in a warehouse is in always consistent analyzable formats. It also provides analytical power and a more complete dataset to base decisions on hard facts. Therefore, decision-makers no longer need to rely on hunches, incomplete data, or poor quality data and risk delivering slow and inaccurate results.

Tools for Data Warehousing

1. Amazon Redshift

Amazon Redshift is an excellent data warehouse product which is a very critical part of Amazon Web Services — a very famous cloud computing platform. Redshift is a fast, well-managed data warehouse that analyses data using the existing standard SQL and BI tools. It is a simple and cost-effective tool that allows running complex analytical queries using smart features of query optimization. It handles analytics workload pertaining to big data sets by utilizing columnar storage on high-performance disks and massively parallel processing concepts. One of its very powerful features is Redshift spectrum, that allows the user to run queries against unstructured data directly in Amazon S3. It eliminates the need for loading and transformation. It automatically scales query computing capacity depending on data. Hence the queries run fast. Official URL: Amazon Redshift

2. Teradata

Teradata is another market leader when it comes to database services and products. Most of the competitive enterprise organizations use Teradata DWH for insights, analytics & decision making. Teradata DWH is a relational database management system by Teradata organization. It has two divisions i.e. data analytics & marketing applications. It works on the concept of parallel processing and allows users to analyze data in a simple yet efficient manner. An interesting feature of this data warehouse is its data segregation into hot & cold data. Here cold data refers to less frequently used data and this is the tool in the market these days. Official URL: Teradata

Tools for Data lakes

1. Amazon S3

The Amazon S3-based data lake solution uses Amazon S3 as its primary storage platform. Amazon S3 provides an optimal foundation for a data lake because of its virtually unlimited scalability. You can seamlessly and nondisruptively increase storage from gigabytes to petabytes of content, paying only for what you use. Amazon S3 has 99.999999999% durability. It has scalable performance, ease-of-use features, and native encryption and access control capabilities. Amazon S3 integrates with a broad portfolio of AWS and third-party ISV data processing tools.

2. Azure Data lake

Azure Data Lake Storage Gen2 is a highly scalable and cost-effective data lake solution for big data analytics. It combines the power of a high-performance file system with massive scale and economy to help you speed your time to insight. Data Lake Storage Gen2 extends Azure Blob Storage capabilities and can handle analytics workloads. Data Lake Storage Gen2 is the most comprehensive data lake available.

Summary

So Which is Better? Data Lake or the Data Warehouse? Both! Instead of a Data Lake vs Data Warehouse decision, it might be worthwhile to consider a target state for your enterprise that includes a Data Lake as well as a Data Warehouse. Just like the advanced analytic processes that apply statistical and machine learning techniques on vast amounts of historical data, the Data Warehouse can also take advantage of the Data Lake. Newly modeled facts and slowly changing dimensions can now be loaded with data from the time the Data Lake was built instead of capturing only new changes.

This also takes the pressure off the data architects to create each and every data entity that may or may not be used in the future. They can instead focus on building a Data Warehouse exclusively on current reporting and analytical needs, thereby allowing it to grow naturally.

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What is Data Lake and How to Improve Data Lake Quality 

What is Data Lake and How to Improve Data Lake Quality 

What is Data Lake and How to Improve Data Lake Quality 

Introduction

Building data pipelines is a core component of data science at a startup. In order to build data products, you need to be able to collect data points from millions of users and process the results in near real-time. Today, many organizations nowadays are struggling with the quality of their data. Data quality (DQ) problems can arise in various ways. Here are common causes of bad data quality:

  • Multiple data sources: Multiple sources with the same data may produce duplicates; a problem of consistency.
  • Limited computing resources: Lack of sufficient computing resources and/or digitalization may limit the accessibility of relevant data; a problem of accessibility.
  • Changing data needs: Data requirements change on an ongoing basis due to new company strategies or the introduction of new technologies; a problem of relevance.
  • Different processes using and updating the same data; a problem of consistency.

In this blog, we are going to look into the world of data lakes and their significance. Furthermore, we will peep into some of the inherent issues in data lakes like quality management. In the end, we will discuss some of the quality measures to control the quality of data in data lakes.

What is Data Lake?

A data lake is a centralized place, like a lake, that allows you to hold a lot of raw data in its native format, structured and unstructured, at any scale. Furthermore, you can store your data as- it is, without having to first structure the data or define it until its needed. Its purpose is for creating reporting dashboards and visualizations, real-time analytics, and machine learning. Also, this can guide better programmatic advertising decisions.

In its extreme form, a data lake ingests data in its raw, original state, straight from data sources. This happens without any cleansing, standardization, remodelling, or transformation. These and other sacrosanct data management disciplines are applicable on the fly. Moreover, it helps in enabling ad hoc queries, data exploration, and discovery-oriented analytics. The early ingestion of data means that operational data is present and made available to analytics as soon as possible. Additionally, the raw state of the data ensures that data analysts, data scientists, and similar users have ample raw material. They can repurpose into many diverse data sets, as needed by unanticipated analytics questions.

Components of Data Lake

A Data Lake is a platform combining a number of advanced, complex data storage and data analysis technologies.

To simplify, we might group the components of a Data Lake into four categories, representing the four stages of data management:

  • Data Ingestion and Storage, that is the capability of acquiring data in real time or in batch, and also the capacity to store data and make it accessible.
  • Data Processing, that is the ability to work with raw data so that they’re ready to be analysed through standard processes. It also includes the capability of engineering solutions that extract value from the data, leveraging automated, periodical processes resulting from the analysis operations.
  • Data Analysis, that is the creation of modules that extract insights from data in a systematic manner; this can happen in real time or by means of processes that are running periodically.
  • Data Integration, that is the ability to connect applications to the platform; in the first place, applications must allow querying the Data Lake to extract the data in the right format, based on the usage you want to make of it

Why use Data Lakes

1. Data Indexing

Data Lakes allow you to store relational data (a collection of data items organized as a set of formally-described tables from which data can be accessed or reassembled in many different ways without having to reorganize the database tables.) — operational databases (data collected in real-time), and data from line of business applications, and non-relational data like mobile apps, connected devices, and social media. They also give you the ability to understand what data is in the lake through crawling, cataloguing, and indexing of data.

2. Analytics

Data Lakes allow data scientists, data developers, and operations analysts to access data with their choice of analytic tools and frameworks. This also includes open source data frameworks such as Apache Hadoop, Presto, and Apache Spark, and commercial offerings from data warehouse and business intelligence vendors. Data Lakes allow you to run Analytics without the need to move your data from one system to another.

3. Machine Learning

Data Lakes will allow organizations to generate different types of marketing and operational insights. It includes reporting on historical data and doing machine learning where models produce forecasts and predictions.

4. Improved Customer Interaction

A Data Lake can combine customer data from a CRM platform with social media data analytics, as well as a marketing platform that includes buying history to empower the business to understand the most profitable audiences, the root of customer churn, and what promotions or rewards could increase loyalty.

The Challenge with Data Lakes

A challenge in data lakes is the inability for analysts to determine data quality because a thorough check-up has not taken place. Also, there is no way to use insights from others who have worked with the data, as there is no account of the lineage of findings by previous analysts. Finally, one of the biggest risks of data lakes is security and access control. Data can be placed into a lake without any oversight, and some of the data may contain privacy and regulatory requirements that other data doesn’t.

Ways to Improve Quality in Data Lakes

1. Use of Machine Learning and NLP

Machine learning can be a game changer because it can capture tacit knowledge from the people that know the data best, then turn this knowledge into algorithms, which can be used to automate data processing at scale. This is exactly how Talend is leveraging Spark machine learning to learn from data stewards during data matching and deduplication of data samples, and then apply it at big data scale for billions of records.

2. Setting the standards for agile data quality

for companies to get the most out of their digital transformation projects and build an agile data lake, they need to design data quality processes from the start. Organisations should focus on standardising the following for maintaining the quality of big data

  1. Roles — Identify roles including data stewards and users of data
  2. Discovery — Understand where data is coming from, where it is going and what shape it is in. Focus on cleaning your most valuable and most used data first
  3. Standardization — Validate, cleanse, and transform data. Add metadata early so data can be found by humans and machines. Identify and protect personal and private organizational data with data masking.
  4. Reconciliation — Verify that data was migrated correctly
  5. Self-service — Make data quality agile, by letting people who know the data best, clean their data
  6. Automate — Identify where machine learning in the data quality process can help, such as data deduplication
  7. Monitor and Manage — Get continuous feedback from users, come up with data quality measurement metrics to improve

3. Employing data quality management frameworks

Another category of frameworks focuses on the maturity of data quality management processes. They aim at assessing the maturity level of DQ management to understand best practices in mature organizations and identify areas for improvement. Popular examples of such frameworks include Total Data Quality Management (TDQM), Capability Maturity Model Integration (CMMI), Control Objectives for Information and Related Technology (CobiT), Information Technology Infrastructure Library (ITIL), and Six Sigma.

data quality management framework

As an example, we can take the TDQM framework. A TDQM cycle consists of four steps, Define, Measure, Analyze, and Improve. The define step identifies the pertinent data quality dimensions. One can quantify them using metrics in the Measure step. Some example metrics are the percentage of customer records with the incorrect address (accuracy), the percentage of customer records with missing birth date (completeness), or an indicator specifying the last update of the customer. The Analyze step tries to identify the root cause of data quality problems. We remedy the previous issues in the improve step. Example actions could be automatic and periodic verification of customer addresses, the addition of a constraint that makes the birth date a mandatory data field, and the generation of alerts when there is no update to customer data in 6 months.

Summary

More and more companies are experimenting with data lakes, hoping to capture inherent advantages in information streams that are readily accessible regardless of platform and business case and that cost less to store than do data in traditional warehouses. As with any deployment of new technology, however, companies will need to reimagine systems, processes, and governance models. Furthermore, if actual data quality improvement is not an option in the short term for reasons of technical constraints or strategic priorities, it is sometimes a partial solution to annotate the data with explicit information about its quality. Such data quality metadata can be stored in the catalogue, possibly with other metadata.

Follow this link, if you are looking to learn more about data science online!

You can follow this link for our Big Data course!

Additionally, if you are having an interest in learning Data Science, click here to start Best Online Data Science Courses 

Furthermore, if you want to read more about data science, you can read our blogs here

How to Discover and Classify Metadata using Apache Atlas on Amazon EMR

The Role of Data Curation in Big Data

What is the Difference Between Hadoop and Spark?