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How Blockchain technology is revolutionising Fintech in 2020

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How Blockchain technology is revolutionising Fintech in 2020

By Vikram

Two decades down under, and the 21st century doesn’t fail to amaze you with its innovation. In fact, the pace of it is so fast, that you inevitably put your hands up and give up keeping track of what is the latest in technology. This phenomenon, in its own ubiquity, manifests itself in Finance, probably more so than anywhere else. One hand to god, let’s admit it, we don’t even refer to Finance as Finance anymore but Fintech (more on that below). While that thought maybe a tad too stretched, Fintech itself is at the cusp of the renovation as if there was a need (Yes Sir)! That flux of change is coming from the headwinds of Blockchain flapping its wings, which happens to be the topic of discussion today:

Blockchain Technology Powering Fintech Revolution

Unless you possess an understanding, be it shallow, of what Fintech is, broadening your viewpoint on the Blockchain (or its implications) would be playing hardball. We’ll limit our definition of the subject so it meets the ends of this blog post.

What is Fintech?

The term that is pushed around, and marketed interchangeably with the now fast-fading term Finance, is a 21 century-incarnate of the latter. Finance, as we all understand, is a domain that deals with the details of money management, more or less. The services revolving around money management are Financial services. Conventional Finance rested on paper bookkeeping until digital transformation hadn’t forced businesses against the wall to modernize legacy systems. When unhindered technological change introduced a way to put legacy systems on fast track mode, that was when Fintech was born.

Finance + Technology = Fintech

In simpler words, when technology finds a way to optimize a traditionally resource-consuming, finance-related task, that comes under the territory of Fintech. We already have a whirlwind of Fintech development that is reshaping Consumer to Business (C2B) interaction and vice versa. The global Fintech microcosm is projected to grow with a CAGR of 24.8%. That estimate cap-sizes the industry’s valuation at $309.98 Billion by 2022.

Blockchain-enabled growth among its service sectors is expected to play a major role in this transformation. If you’re new to the concept of Blockchain, you’ll find our in-depth guide on the topic helpful. For this post, its a touch and go for a Blockchain overview.

What is Blockchain?

Blockchain is an ever-growing list of records run on a network. Its system architecture is no different from a database. The records are called blocks cryptographically linked to one another forming a chain. The credibility of the chain is maintained in that the mathematical hash of the last block will be found in the subsequent block. The blocks are added to the network, depending upon the consensus mechanisms deployed by the Blockchain developers. Further properties attributable to the Blockchain include:

  • Decentral – No central authority enforces the rules of engagement, placing the trust in the hands of the participating nodes that run the network. 
  • Permissionless – Anyone can join the network with the requisite computational (mining) power in validating transactions and earning rewards as cryptocurrencies/tokens. 
  • Data Tampering –  Data once recorded using the blockchain technology is unchangeable, at least in theory. The blocks are immutable and near impossible to impose new data on.  

blockchain technology

Contemporaneous developments in the Blockchain Technology make it a multi-functional concept, one that the Fintech technology can take justified advantage of. Here’s how its service sectors could harness that power:

1. Payments – Instant Cross-Border transfers

Case

Cross-border payments are a chronic pain-point for Banks who’re parallyzed by a lethargic and snail-paced process. In some cases, cross border payments take up to a week to be realized. The middlemen have a crucial foothold on transfer fees charging anywhere in the region of 5-20%. Similarly peer-to-peer fintech applications in the market limit transfers within restricted geography taking their respective slice of transfer fees.

There has to be a better way to stay devoted to regulatory obligations and processing payment transfers faster. Is there?

Solution

Financial institutions are analysing the prospect with a Permissioned-style template of the Blockchain technology. They’ll act as the central authority propagating the rules for remittance over the blockchain. As per Deloitte, blockchain based payments from business-to-business and peer-to-peer results in 40% – 80% reduced transaction costs. They’re also settled within seconds. Yes, it would be a paradigm shift but as per a projection by Mckinsey & Co. blockchain could drive $50 – $60 Billion in transcontinental B2B and $3 – $5 Billion in P2P payments respectively. 

Example

Westpac and Australian Bank partnered with Ripple, an Enterprise Blockchain company for cross border payments. Wirex is another Fintech company integrating blockchain into its workflow. Its a standalone vendor allowing instant international remittances. Users can avail of the mobile application for purchase orders selecting from 12 (total) fiat and cryptocurrencies. Wirex designed a 2-way Bitcoin debit card with a Visa debit card soon to be released easing point of sale transactions.

2. Stock Exchanges – Real-Time Settlements

Case

There is a lot of conjecture around eliminating third parties from this space but truth be told, Stock markets wouldn’t move a dime without them. An atypical scenario – you sell shares today, but the ownership certificate is not merited until T+2 days, where ‘T’ is the day when you sold the shares. The lag is owing to a few operational bottlenecks such as regulatory approvals, and mandatory clearances. Not to mention the cost of the brokerage eventually levied on the customer in commission fees.

Solution

The Fintech Blockchain marriage could wipeout such intermediaries with decentralization where the dystopian exchange runs on nodes dispersed around the globe. They would earn DEX tokens for keeping the network up and running. 

The Blockchain technology would assume its pure potential if interoperability is achieved. Once that happens, retail or daily traders with small orders could be stashed in local groups, by partitioning the blockchain into smaller ‘shards’. Order calls will be recorded entirely on the sidechains, running parallelly while only the transfer of certificate will be recorded onto the main blockchain. The result – increased transactional volume and low network redundancy.  

Example

DEX, Decentral Cryptocurrency Exchanges like Changhero, Waves Dex and OpenLedger Dex are powering this subset of the Fintech revolution forward. Their algorithms effectuate peer-to-peer trading. Being non-custodial in nature, funds are transferred directly to the users’ wallet, reducing the risk of online heists. The barrier to entry is low for retail customers due to lack of background checks, however, decentralized crypto exchanges often face liquidity issues for pairs with low trading volume.

3. Trading – Automated with Smart Contracts

Case

Like we said in the beginning, conventional Finance is chained to paperwork, perhaps irrevocably. Shipping, for instance, requires client-side formalities like lading bills, invoices, and the letter of credit. The industry has so far leveraged software as a service for internet-enabled settlements, yet the entire process gasps for breath and could be put on Fintech Blockchain Technology steroids.

Solution

Smart contracts seem to be the last piece of the puzzle here. They are programmable codes that automate the transfer of tokens (cryptocurrencies) over a blockchain and will ensure the funds move from B2B only when coded preconditions are satisfied. Paperwork could be reduced exponentially, probably to the extent of no use at all, reducing carbon footprints. This requires large scale enterprise migration onto and agreed upon Blockchain protocols the signs of which look promising. 

Example

IBM & Maersk collaboration for a global trade platform to find scalable solutions of Blockchain in Fintech. Moreover, Forbes released its report of Top 50 Billion-Dollar companies who’re exploring the scope of implementing blockchain solutions. Over half of them are consulting Ethereum. The Ethereum Virtual Machine (EVM) executes peer-to-peer smart contracts with the networks’ de facto cryptocurrency namesake, Ethereum. Developers can also create decentralized applications over the protocol.

4. Crowdfunding – Regulated Token Purchase For All

Case

Fintech ushered in a new age for raising funds, but Blockchain in Fintech took it a notch up.
Fintech savvy people need no reminding of the Initial Coin Offering bubble. They proved a drooling prospect because investors could buy into a venture purchasing tokens instead of shares, non-taxed.

The tokens were not categorized as securities and hence circumvented regulatory oversight. The tokens, tradeable through crypto-exchanges, had utility underpinned as their USP. As with any security, speculation influenced their prices, which soared after a pump of marketing gimmicks. The same tokens were then dumped, by the investor who’d sell on a high or the founders who’d often go absconding. Apparently, 80% of the projects turned out scam.

Solution 

The market has evolved since. The new-age Fintech Blockchain avatar has rebranded itself as Security Token Coins. They’re every bit the ICOs were in an operational sense, plus the veneer of regulation by the United States Securities & Exchange Commission. STOs will allow fractional ownership of shares, cross-border investment opportunities, and purchase of securities all approved by the government.

Example

Blockchain Capital ran an STO campaign in accordance with the US SEC and raised $10 million. Those buying into the offering will reap dividends just like any other investor, without staking more than their allowance threshold. Were there no STOs, you’d have to be an accredited investor with an annual salary of $200,000 to participate in the fund.

5. Syndicated Lending –  Seamless Data Verification

Case

A syndicate is the coming together of companies for a common cause, which in this case is lending capital to individuals. Consider a bank which can take up to weeks if not months, disbursing loans. While the evaluation approach may be multi-pronged and lengthy, all financial institutions are ordained by the government to authenticate identity backgrounds. This begins with a Know Your Customer verification often summing up with the customers complying to Anti Money Laundering guidelines.

Perhaps, we’ve already been through the agony of performing this mechanically repetitive process at one Bank after the other.

Solution

Fintech and blockchain could work hand in hand. There could be a standard Blockchain protocol that the syndicate partners, banks, have assented to join. This protocol would store user credentials such as those required by its partners. Upon the completion of a background check by one Bank, others need not follow suit i.e., if the same customer wants to avail a service. Time consumption will be reduced by a factor of the multitude.  

Example 

Fusion LenderComm is a platform for syndicated loans that’ll run on R3’s open-source Corda Blockchain. They focus on increasing lenders real-time access to information, helping them process loans faster. Syndicate partners get account access to Finastra’s Fusion Loan IQ that shares crucial data points like the position information, credit agreements and accrual balances real time. This simplified agent-to-lender communication will introduce transparency and efficient loan disbursement frequency.

6. Accountancy – Blockchain as an ‘Electronic Notary’

Case

Auditing hinges on time consumption as reconciliation requires both expert manpower input and abides by uncompromisable regulatory protocols. Consider double entry bookkeeping for a moment. For every debit entry made in one register, there ought to be credit in a second register. From record entry to tallying, imagine the hours it would take annual billings to be fact checked and rectified. But how will Blockchain help?

Solution

A blockchain is more than just a database. Its architecture and block validation prohibits double spending. Instead of multiple records for every transaction receipt, we can have an integrated trail on a Blockchain, with entries segregated into categories. They’ll have the added protection from cryptography. Auditors could look at a combined array of financial statements whose authenticity can be verified by electronic signatures. 

Example

PwC Blockchain Validation Solution. It would be a software that would act as a single node on the Blockchain protocol of the client. Users could customize the same to validate transactions automatically and flag the ones that need further review. Stakeholders with access to the system can build reports from dashboards.

Conclusion

It’s not a question of choosing between Fintech and Blockchain. We know one complements the other. In addition to that, the conversation from yesteryears has switched from whether Blockchains are reliable to integrating them with business legacy systems. Enterprises have a visible interest in the field application of this technology, but the Fintech Blockchain duo has proximity towards startups pioneering innovation in the field. This macro trend unfolding before us testifies to the fact that early adopters will be the greatest beneficiaries in a market that is still in its formative stages. 

Source: https://www.fintechnews.org/how-blockchain-technology-is-revolutionising-fintech-in-2020-2/

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Join Hands with Instagram’s New Algorithm to Boost Your Business’s User Engagement

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👉 Most people are not at all happy with the latest 📸 Instagram algorithm. However, if you want to make the most of your business, you should understand how it works. The trick here is you must work with its algorithm and not go against it here; it is easy for your know-how. 👇🔻

🚩This post will guide you on how the 🆕 new Instagram algorithm works and how you can use it for the advantage of your business-

How does the new Instagram algorithm work?

The new 📸 Instagram ♀️ algorithm is a mystery to many users on the platform. It no longer functions at a slow pace as it did in the past. To score high on Instagram today, you need to be extremely diligent as a business owner. The algorithm needs to be fully optimized so that you get success in the platform. So, read and remember the following factors to help you determine how your posts can perform well to boost user engagement and sales revenue for your business with success-

The volume of social engagement you receive -: Note, your posts with the maximum number of shares, views, comments, etc., will rank high in the Instagram feed over others. When your post receives a lot of comments and likes, the Instagram platform gets a signal that it is engaging to users and has high-quality content. It wants more users to see it, and the algorithm on the platform will display it to other users online.

However, here again, there is a catch. It is not only the engagement that Instagram considers now; it is how fast your Instagram post engages the readers in some cases. Trending hashtags on Instagram, for instance, is the best-known example of the above. The volume of engagement you get for your business posts is important; however, how quickly you get this engagement is even more important!

👉▶Tip- You should discover when the best time of the day to post is. In this way, you can schedule posts targeted at that time only. You should post when most users can see it. This increases your chances of boosting engagement faster, and the Instagram algorithm as it knows what somebody likes on Instagram and will take over from here to share it with those users who will ✌ ⚡ like, share and comment on your post.

How long are people looking at your post on Instagram -: If you see the algorithm that Facebook uses, it takes into account how long users look at your post and spend time interacting with its content. Instagram is no different! Its algorithm will take a look at the duration of time that users spend on your post too. This is again, another important factor that Instagram uses to boost posts.

👉▶Tip- Here, you should craft a great 📸 Instagram caption for your post to boost user engagement. If your Instagram caption is engaging, users will want to read it or click on the button that says “more” to spend more time on your post, and in this way, you can boost more engagement with success on  📸 Instagram.

This is why Boomerangs and videos are generally posted in the 📹 video format. This makes them perform well with the Instagram algorithm. Users take more time to watch them toll the end. Another great way for you to make users stay on your posts is to swipe up CTA action for them to view more. This is another great strategy you can use for your business to boost engagement.

When did you post your photo?-: This is another factor that influences how the 📸 Instagram algorithm works for your business is determining the time of the post. It depends on how often you use the 📲 app. In the past, the algorithm used to give you insights into the recent posts; however, if you tend to log on to Instagram just a few times in one week, it will show posts that have been posted recently. You might even get likes from a previous post that you published a few days ago. The target here is to keep users updated on the posts they might have missed due to the fact that they did not log in regularly.

👉 This means that users can see your posts now for longer periods.

The sort of content you post also influences engagement – Think about if 📸 Instagram only focused on content with the optimal engagement; users would only see that content every time they logged into 📸 Instagram. However, this is not the case with the Instagram algorithm.

For instance, the content 👥 users’ genre searches for in the platform also influence the algorithm of how Instagram works. If a user is a fan of sports, says the NBA and sees that genre of content regularly, Instagram will immediately catch on to this and bring the user more similar content related to sports and the NBA. It knows that the user will be interested in such posts and perhaps bring news and updates of the LA Lakers to boost 👤 user engagement and satisfaction.

Accounts that users search for -: Like the above, the accounts that users search for also determines how the 📸  Instagram algorithm works. This is why when users search a specific 🧾 account many times; Instagram will bring more such content from other accounts to that user. This is why they see it often in their Instagram feeds.

From the above, it is evident that if you want to work with the new Instagram algorithm, you must understand how it works and optimize your posts to help it boost your business. In the past, the feed on Instagram was chronological; however, things have changed now.

🟥 So, ensure that your CTA is strong; you use the right hashtags, post at the best time, and make your feed on Instagram as attractive as possible. In this way, you can boost user engagement, lead conversions, sales, and of course, gain a strategic edge in the market as well.

↘ Source: Ron Johnson. Ron is a Marketer. He always shares his tips on trendy marketing tricks. He always implements new tricks in his field.

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Top 10 Big Data trends of 2020

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Top 10 Big Data trends of 2020

By Priya Dialani

During the last few decades, Big Data has become an insightful idea in all the significant technical terms. Additionally, the accessibility of wireless connections and different advances have facilitated the analysis of large data sets. Organizations and huge companies are picking up strength consistently by improving their data analytics and platforms.

2019 was a major year over the big data landscape. In the wake of beginning the year with the Cloudera and Hortonworks merger, we’ve seen huge upticks in Big Data use across the world, with organizations running to embrace the significance of data operations and orchestration to their business success. The big data industry is presently worth $189 Billion, an expansion of $20 Billion more than 2018, and is set to proceed with its rapid growth and reach $247 Billion by 2022.

It’s the ideal opportunity for us to look at Big Data trends for 2020.

Chief Data Officers (CDOs) will be the Center of Attraction

The positions of Data Scientists and Chief Data Officers (CDOs) are modestly new, anyway, the prerequisite for these experts on the work is currently high. As the volume of data continues developing, the requirement for data professionals additionally arrives at a specific limit of business requirements.

CDO is a C-level authority at risk for data availability, integrity, and security in a company. As more businessmen comprehend the noteworthiness of this job, enlisting a CDO is transforming into the norm. The prerequisite for these experts will stay to be in big data trends for quite a long time.

Investment in Big Data Analytics

Analytics gives an upper hand to organizations. Gartner is foreseeing that organizations that aren’t putting intensely in analytics by the end of 2020 may not be ready to go in 2021. (It is expected that private ventures, for example, self-employed handymen, gardeners, and many artists, are excluded from this forecast.)

The real-time speech analytics market has seen its previously sustained adoption cycle beginning in 2019. The idea of customer journey analytics is anticipated to grow consistently, with the objective of improving enterprise productivity and the client experience. Real-time speech analytics and customer journey analytics will increase its popularity in 2020.

Multi-cloud and Hybrid are Setting Deep Roots

As cloud-based advances keep on developing, organizations are progressively liable to want a spot in the cloud. Notwithstanding, the process of moving your data integration and preparation from an on-premises solution to the cloud is more confounded and tedious than most care to concede. Additionally, to relocate huge amounts of existing data, organizations should match up to their data sources and platforms for a little while to months before the shift is complete.

In 2020, we hope to see later adopters arrive at a conclusion of having multi-cloud deployment, bringing the hybrid and multi-cloud philosophy to the front line of data ecosystem strategies.

Actionable Data will Grow

Another development concerning big data trends 2020 recognized to be actionable data for faster processing. This data indicates the missing connection between business prepositions and big data. As it was referred before, big data in itself is futile without assessment since it is unreasonably stunning, multi-organized, and voluminous. As opposed to big data patterns, ordinarily relying upon Hadoop and NoSQL databases to look at data in the clump mode, speedy data mulls over planning continuous streams.

Because of this data stream handling, data can be separated immediately, within a brief period in only a single millisecond. This conveys more value to companies that can make business decisions and start processes all the more immediately when data is cleaned up.

Continuous Intelligence

Continuous Intelligence is a framework that has integrated real-time analytics with business operations. It measures recorded and current data to give decision-making automation or decision-making support. Continuous intelligence uses several technologies such as optimization, business rule management, event stream processing, augmented analytics, and machine learning. It suggests activities dependent on both historical and real-time data.

Gartner predicts more than 50% of new business systems will utilize continuous intelligence by 2022. This move has begun, and numerous companies will fuse continuous intelligence during 2020 to pick up or keep up a serious edge.

Machine Learning will Continue to be in Focus

Being a significant innovation in big data trends 2020, machine learning (ML) is another development expected to affect our future fundamentally. ML is a rapidly developing advancement that used to expand regular activities and business processes

ML projects have gotten the most investments in 2019, stood out from all other AI systems joined. Automated ML tools help in making pieces of knowledge that would be difficult to separate by various methods, even by expert analysts. This big data innovation stack gives faster results and lifts both general productivity and response times.

Abandon Hadoop for Spark and Databricks

Since showing up in the market, Hadoop has been criticized by numerous individuals in the network for its multifaceted nature. Spark and managed Spark solutions like Databricks are the “new and glossy” player and have accordingly been picking up a foothold as data science workers consider them to be as an answer to all that they disdain about Hadoop.

However, running a Spark or Databricks work in data science sandbox and then promoting it into full production will keep on facing challenges. Data engineers will keep on requiring more fit and finish for Spark with regards to enterprise-class data operations and orchestration. Most importantly there are a ton of options to consider between the two platforms, and companies will benefit themselves from that decision for favored abilities and economic worth.

In-Memory Computing

In-memory computing has the additional advantage of helping business clients (counting banks, retailers, and utilities) to identify patterns rapidly and break down huge amounts of data without any problem. The dropping of costs for memory is a major factor in the growing enthusiasm for in-memory computing innovation.

In-memory innovation is utilized to perform complex data analyses in real time. It permits its clients to work with huge data sets with a lot more prominent agility. In 2020, in-memory computing will pick up fame because of the decreases in expenses of memory.

IoT and Big Data

There are such enormous numbers of advancements that expect to change the current business situations in 2020. It is hard to be aware of all that, however, IoT and digital gadgets are required to get a balance in big data trends 2020.

The function of IoT in healthcare can be seen today, likewise, the innovation joining with gig data is pushing companies to get better outcomes. It is expected that 42% of companies that have IoT solutions in progress or IoT creation in progress are expecting to use digitized portables within the following three years.

Digital Transformation Will Be a Key Component

Digital transformation goes together with the Internet of Things (IoT), artificial intelligence (AI), machine learning and big data. With IoT connected devices expected to arrive at a stunning 75 billion devices in 2025 from 26.7 billion presently, it’s easy to see where that big data is originating from. Digital transformation as IoT, IaaS, AI and machine learning is taking care of big data and pushing it to regions inconceivable in mankind’s history.

Source: https://www.fintechnews.org/top-10-big-data-trends-of-2020/

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What are the differences between Data Lake and Data Warehouse?

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Overview

  • Understand the meaning of data lake and data warehouse
  • We will see what are the key differences between Data Warehouse and Data Lake
  • Understand which one is better for the organization

Introduction

From processing to storing, every aspect of data has become important for an organization just due to the sheer volume of data we produce in this era. When it comes to storing big data you might have come across the terms with Data Lake and Data Warehouse. These are the 2 most popular options for storing big data.

Having been in the data industry for a long time, I can vouch for the fact that a data warehouse and data lake are 2 different things. Yet I see many people using them interchangeably. As a data engineer understanding data lake and data warehouse along with its differences and usage are very crucial as then only will you understand if data lake fits your organization or data warehouse?

So in this article, let satiate your curiosity by explaining what data lake and warehousing are and highlight the difference between them.

Table of Contents

  1. What is a Data Lake?
  2. What is a Data Warehouse?
  3. What are the differences between Data Lake and Data Warehouse?
  4. Which one to use?

What is a Data Lake?

A Data Lake is a common repository that is capable to store a huge amount of data without maintaining any specified structure of the data. You can store data whose purpose may or may not yet be defined. Its purposes include- building dashboards, machine learning, or real-time analytics.

data warehouse data lake

Now, when you store a huge amount of data at a single place from multiple sources, it is important that it should be in a usable form. It should have some rules and regulations so as to maintain data security and data accessibility.

Otherwise, only the team who designed the data lake knows how to access a particular type of data. Without proper information, it would be very difficult to distinguish between the data you want and the data you are retrieving. So it is important that your data lake does not turn into a data swamp.

data warehouse data lake

Image Source: here

What is a Data Warehouse?

A Data Warehouse is another database that only stores the pre-processed data. Here the structure of the data is well-defined, optimized for SQL queries, and ready to be used for analytics purposes. Some of the other names of the Data Warehouse are Business Intelligence Solution and Decision Support System.

What are the differences between Data Lake and Data Warehouse?

Data Lake Data Warehouse
Data Storage and Quality The Data Lake captures all types of data like structure, unstructured in their raw format. It contains the data which might be useful in some current use-case and also that is likely to be used in the future. It contains only high-quality data that is already pre-processed and ready to be used by the team.
Purpose The purpose of the Data Lake is not fixed. Sometimes organizations have a future use-case in mind. Its general uses include data discovery, user profiling, and machine learning. The data warehouse has data that has already been designed for some use-case. Its uses include Business Intelligence, Visualizations, and Batch Reporting.
Users Data Scientists use data lakes to find out the patterns and useful information that can help businesses. Business Analysts use data warehouses to create visualizations and reports.
Pricing It is comparatively low-cost storage as we do not give much attention to storing in the structured format. Storing data is a bit costlier and also a time-consuming process.

Which one to use?

We have seen what are the differences between a data lake and a data warehouse. Now, we will see which one should we use?

If your organization deals with healthcare or social media, the data you capture will be mostly unstructured (documents, images). The volume of structured data is very less. So here, the data lake is a good fit as it can handle both types of data and will give more flexibility for analysis.

If your online business is divided into multiple pillars, you obviously want to get summarized dashboards of all of them. The data warehouses will be helpful in this case in making informed decisions. It will maintain the data quality, consistency, and accuracy of the data.

Most of the time organizations use a combination of both. They do the data exploration and analysis over the data lake and move the rich data to the data warehouses for quick and advance reporting.

End Notes

In this article, we have seen the differences between data lake and data warehouse on the basis of data storage, purpose to use, which one to use. Understanding this concept will help the big data engineer choose the right data storage mechanism and thus optimize the cost and processes of the organization.

The following are some additional data engineering resources that I strongly recommend you go through-

If you find this article informative, then please share it with your friends and comment below your queries and feedback.

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Source: https://www.analyticsvidhya.com/blog/2020/10/what-is-the-difference-between-data-lake-and-data-warehouse/

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