A data lake is a type of repository for structured and unstructured data. The data volumes and nature aren’t of concern. You don’t have to put the information in order before you enter it into the lake. Data scientists find that quality rather useful. The data lake architecture also allows various kinds of data analytics. Dashboards and visualizations are just a part of the methods you can employ. Big data processing and real-time analytics would’ve been much harder without data lakes.

Data Lake Vs. Data Warehouse

Big data is a relatively new phenomenon. Even business managers don’t know much about data lakes and warehouses. At the same time, they are both crucial to business intelligence. The difference between the two terms may not be evident from the start. Both play a role in data governance, though not the same one. If you want your organization to handle raw data beneficially, you need to be aware of the difference. 

What’s a Data Warehouse?

A data warehouse, like a data lake, is a repository for information. Various data sources supply the data sets to a warehouse. However, data scientists and managers use it to access current and historical data. They become the foundation of future analysis that determines the future of an organization. A data warehouse has several other intrinsic qualities:

• The data needs structure before it enters the warehouse
• Before you enter data, you need to define its use
• It follows a strictly pre-defined methodology
• It offers an abstract picture of an organization, based on a subject area

A data warehouse is useful to companies. It has the potential to enhance the way an organization operates. Increased profits and decreased expenses are a direct result of the analysis warehouses allow.

The Intrinsic Qualities of a Data Lake

Data lakes, on the other hand, possess different traits. They enable information handling in a much freer manner. That type of ecosystem allows business users to:

• Enter information from data sources in its natural state
• Use data without transforming it or transforming it just a little (semi-structured data)
• Employ schema to carry out big data analytics

A lake provides opportunities for better data processing and easy consumption. There are several key elements in which it differs from a data warehouse. 

A Data Lake Keeps All Information

Before you enter data into a warehouse, you need to:

• Analyze the data source
• Understand the processes the business will use it for
• Create a data profile

As a result, you create a highly structured data set. Its purpose is to accommodate the reporting on concrete outcomes. Not all data available will enter the warehouse. The reason for that approach is it saves time and resources. It’s the opposite of a data lake. It also takes in all data, not just what you’re using for a particular analysis. The idea is that you might need that chunk of info in the future. So, you have to preserve it in some way. Data in the lake has no expiration date, either. Unless you choose to delete it, it’ll remain forever. The hardware for data lake storage differs as well. The huge volumes of data stored make it not viable to use high-end servers and hard-drives. Experts prefer cheap, off-the-shelf solutions for data lakes.

All Sorts of Data are Game

Transactional systems and quantitative metrics play an important role in data warehouses. However, data lakes don’t share that quality. You can put all types of information in a lake, including:

• Web server logs
• Sensor data
• Social media activity
• Text
• Images

Because data lakes are cheaper, you can afford to keep such data. The idea is you might not know exactly how to use it right now. That doesn’t mean you won’t find out at some point. 

Data lakes’ design accommodates non-traditional data types well. The sources and structure don’t matter that much. Raw data remains raw until you decide to use it. It’s what makes data lakes cheap enough to maintain. Data management experts call the approach “Schema on Read.” In contrast, data warehouses use “Schema on Write.”

All Users Can Access a Data Lake

A data lake is perfect for all types of users. To understand that, you need to know the way different users utilize data in an organization:

• The majority consists of maybe 80% of the organization’s members. They need access to the data to make reports or compare metrics. They also benefit from structured data, like a spreadsheet
• Some 10% analyze the data. They employ both structured and unstructured sets. It isn’t rare for them to tap directly into the data sources. The reports they prepare find distributions throughout the organization
• Data scientists are the remaining 10%. Their research often creates wholly new sources. They try to answer questions and make new findings through deep analysis. They also follow statistical and predictive models.

Handling data management with the data lake approach benefits all these people. It can store structured information suitable for most users. At the same time, data scientists can find raw unstructured info there as well. 

Data Lakes Are Adaptable

Data warehouses suffer from one major drawback. It takes forever to apply changes to the system. However, the reason is that it takes a lot of time to build the initial structure. On the one hand, performing analysis and compiling reports is easy. On the other, it takes a lot of developer time to introduce changes. 

A data lake eliminates the waiting period you have to suffer through. Therefore, if your business has to address a question, it can do it right away. When you introduce changes to the data lake, you don’t save them immediately. First, you can check if they address the issue you want to resolve. 

Faster Insights

The data lake approach means:

• You have access to all types of data types
• Users can access everything faster
• There’s no need to cleanse, transform, and structure the data

Due to these reasons, a data lake gives you access to insights faster than a warehouse. Note, most data structures in the lake exist in the form of metadata. Users need a set of skills and desire to explore to analyze the data lake. It’s a viable option for specialists. In an organization with a large team, it’s not always the most adequate option. 

Is a Data Lake a Relational Database?

E.F. Codd proposed the relational data model in 1970. It’s not a complex concept. The relations in question are, more or less, tables. The user also stores the data in columns and rows in the tables. Each row has a key that helps identify it. Records and tuples are other terms for rows. Columns go by the name of attributes. A relational database is one that follows that model. Most relational database systems use SQL (Structured Query Language). 

While a data lake may contain structured data, it leaves room for an unstructured one as well. So, it won’t be accurate to say that they are relational databases in nature.

Will the Data Lake Replace the Warehouse?

Data lakes have many positive sides, but they aren’t ideal in each situation. It isn’t likely that data lakes will replace data warehouses any time soon. The two models complement each other. The lake doesn’t eliminate the need for a warehouse. The structure of the data warehouse makes it easy to get answers to predictable questions. They quickly provide the relevant information on matters including:

• Revenues
• Regional distribution of sales
• YoY change in sales
• Trends
• Business performance

Data lakes come in handy when you need more complicated data analysis. It gives data scientists the agility to break new ground. They can help your organization move forward. 

So, What Should You Choose?

If you already have a solid data warehouse, it won’t be wise to throw it away. No system is perfect, though. Your warehouse might suffer from imperfections. In such a case, opening a data lake to complement it may do the trick. Use the data warehouse the way you’ve been using it. Fill the data lake with new data sources. Running both alongside each other will give you more information than ever. It means you will be in a position to conduct more in-depth data analytics. The hardware of your data warehouse is bound to age. When that starts, you may consider transferring it to the data lake. The hybrid approach will give you the best of both worlds. At the same time, your expenses will drop.

Big Data and the Data Lake

Big data plays an ever-increasing role in the way organizations work. The Internet of Things (IoT) alone produces more data in a month than previous technology did in a year. Low-cost solutions for data management and functionality become a must. Modern data science is another thing that can’t function without data lakes. The lake offers cost-effective access to a large number of data sources. It gives companies more freedom at a better cost. So, it comes as no surprise that data lakes have become an integral part of the way we do big data analytics.

Data Silos

A data silo is a special type of data lake. What distinguishes a silo is that only one organization has access to its data sources. However, it’s a good solution for companies that want to stay ahead of the competition. Data silos also find application in research. They help scientists preserve the data they work on at a given time. It’s particularly useful when patented work is involved. It makes less sense using a data silo for open-source projects.

What Happens to Unattended Data Lakes?

However, the power to compute huge chunks of unstructured data is the strength of data lakes. It’s a good enough reason in itself to endorse the model. What happens if you neglect the maintenance of your enterprise data? Experts call an unmanaged or deteriorated data lake a data swamp. In most cases, data swamps provide users little value. Sometimes they even can’t get proper access to them. 

How Does a Data Lake Turn into a Swamp? 

The shortest route is through a business leaving a data lake unattended. Oversaturation and little curation efforts can hamper the useful utilization of the data sets. However, there is an important note to take. Avoiding turning your data lake into a data swamp may not be enough in itself. To utilize its capabilities, you’ll have to build a solid Data Strategy. These are the points to focus on and the results you’re after. Also, enough metadata to establish the context of the sets is necessary. If you aren’t sure how to do that, don’t sweat it. Dexivo’s trained experts are ready to guide you through the process.

How Much Would a Data Lake Cost?

We’ve already mentioned data lakes cost less than data warehouses. The question is, how much less? It all depends on the requirements of your organization. How many data sources are you going to use? What type of data are we talking about? Do you have any specific requirements concerning the physical location of the data servers? Answer these questions and contact service providers for quotes. Some of the biggest names on the market today include:

• Snowflake
• Amazon
• Azure

At Dexivo, we work with all major service providers. We can accommodate your transition to a data lake. Our experts know everything about data integration and management. Don’t hesitate to reach out for help. 

Snowflake

Snowflake is a cloud data lake infrastructure. It has a multi-cluster structure, and many organizations are implementing it as their sole system for business intelligence. IAC Publishing Labs, therefore, made getting high data quality relatively easy. They offer:

• Elasticity – being able to assign compute resources of any amount to any user
• Cheap data storage
• Consistency – reliable multi-statement transactions
• Security and good data protection

Snowflake is quickly gaining on other data marts, data warehouses, and data lake providers. It still has a long way to go to dethrone Microsoft, for example, but it’s a viable option. 

Amazon S3

Amazon S3 is part of the AWS package that offers object storage. It has an easy-to-use web interface and has a lot of features. Amazon S3 is suitable for storing a variety of data:

• Internet applications
• Backup and disaster recovery
• Data archives
• Data Lakes
• Hybrid data storage

Using AWS for data lake purposes has an obvious advantage. Amazon is the world’s largest corporation. You can expect a long lifecycle of the service. They as well as offer reliable customer support and competitive prices, as well. S3 utilizes Hadoop to maintain structured and unstructured databases. Hadoop is an open-source Apache server with an excellent reputation. Another advantage of Hadoop-based systems is their scalability capabilities. They make it well-suited to working with big data. 

Azure 

Azure Data Lake is a data analytics service with reliable scalability capabilities. It uses YARN, a Hadoop-based technology, and has other features. Azure started their data lake service in 2016. The idea is to provide a stable environment to store data from:

• Azure
• AdCenter
• Bing
• MSN
• Skype
• Windows Live

Users can also employ Azure Data Lake as data storage for info from a variety of sources. The Azure Data Analytics allows you to run an on-demand analysis of the data. Pentaho integration comes in handy as well. The package uses U-SQL, a combination of SQL and C#. It’s a powerful query language. However, specialists suggest it does a better job than previous languages. 

With Azure, Microsoft promises:

• Storage space of petabytes of information and objects
• Easy development of massively parallel programs
• Reliable capabilities to debug and optimize
• Pay-per-use model
• Speed and instant scalability
• Impenetrable security on an enterprise-level

MS has a reputation for delivering on its promises. Give Azure a try if you are looking for a long lifecycle data lake. There are also many subscription options, and the pay-per-use model allows you to test its features cheaply.

Why Do You Need a Data Lake?

The model gives your employees web-based access to huge volumes of data. Through API, you can also compute all information you require in real-time. There’s no doubt proper business intelligence would benefit your company. However, the proper way to do it is through reliable data analytics. The data lake is cheap, dependable, and has great access control. It enables you without hiccups to:

• Get hold of unstructured data in real-time
• Use cases
• Employ easy access control

At Dexivo, we believe lakes are the future of cloud technology. While most platforms are relatively new, they catch up to the business intelligence needs. Soon, you won’t be able to deal with big data without one. 

Don’t hesitate to call Dexivo when you have questions about data lakes. Our experienced experts have the skills and know-how to help you out. Take your company to a new level by using the latest data analytics trends.

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