The digital universe is doubling every year and is expected to reach 44 trillion gigabytes by 2020. Up to 90 percent of this data is unstructured or semi-structured. This is a double challenge: find a way to store everything and maintain fast processing capacity. Then here Comes the data lake.
Data lakes have become a major component for organizations migrating to modern data platforms as they scale data operations and machine learning initiatives. The data lake infrastructure provides users and developers with access to self-service for diversified or closed traditional information.
Today, with the advent of cloud computing, businesses and data teams can measure new projects based on return on investment and individual workload to determine whether projects need to be scaled. Production readiness and cloud computing security is one of the biggest business achievements today. This article offers almost unlimited possibilities for an enterprise analytical life cycle of data lake and its architecture.
What is a data lake definition?
Data lakes are central data warehouses where conventional structured data (rows and columns) and unstructured non-tabular raw data can be stored in their original format (such as videos, images, binary files, etc.). Data lakes are used cheaply to store objects and open formats so that many applications can use the data.
It is used to combine all of an companies data into one central location that can be stored without the need to create up front schemas or structures. Data in all stages of the refinement process can be stored in data pools: Raw data can be retrieved and stored right next to enterprise structured data sources (e.g. database tables) and intermediate data tables. produced in the process of refining raw data.
Why a Data Lake?
The main purpose of building a data lake is to provide an obscure view of data from data scientists. The reasons for using a data lake are:
- With the advent of storage machines like Hadoop, it became easy to store a lot of information. There is no need to create an enterprise archiving model with a Data Lake.
- As the volume of data increases, the quality of data and metadata increases, the quality of the analysis also increases.
- Data lakes offer business flexibility
- To make profitable predictions
- This offers the executive organization a competitive advantage.
- There is no silo data structure. Data Lake provides customers with a 360 degree view and makes analysis more stable.
Companies that successfully generate business value from their data outperform their peers. A study shows that companies using Data Lake revenue growth by 9%. These executives are capable of performing new types of analysis such as machine learning on new sources such as logs, click stream data, social media, and internet-connected devices stored in data pools. This has helped them identify and take advantage of opportunities for faster business growth by attracting and retaining customers, increasing productivity, proactively maintaining equipment, and making the right decisions.
Organizations today have a lot of data, but are often isolated and enclosed in a variety of storage systems: data warehouses, databases, and other storage systems in the company. Data lakes break down these data silos by centralizing and consolidating all batches and flows of your company’s assets into one comprehensive and authoritative repository for up-to-date analysis. Consolidating all of your data in a data pool is the first step for any company looking to leverage machine learning and data analysis to make money over the next decade.
The flexible and unified data lake architecture opens up many new uses for multi-functional business analytics projects, BI and machine learning projects that can generate enormous business value. Data analysts can use SQL to get large queries by querying data pools. Scientists can collect and enrich data sets to produce ML models with increased accuracy. Data engineers can create automated ETLs, pipelines, and business intelligence analysts can create visual dashboards and reporting tools faster and easier than ever before. All of these uses can be done in the data pool at the same time without having to lift and move data, even as new data is entered.
Data lake versus data warehouse
Depending on the requirements, an organization will generally need a data warehouse and a data lake because they have different requirements and uses.
The data warehouse is a database are two different strategies for storing big data that is optimized for analyzing relational data from transactional systems and various business applications. Data structures and schemas are predefined to optimize high-speed SQL queries, the results of which are typically used for operational reporting and analysis. Data is cleaned, enriched and modified so that it acts as the “only source of truth” users can trust.
Data lakes are different in that they store relational data from various business applications and non-relational data from mobile applications, IoT devices, and social media. The data structure or schema is not determined when the data is collected. This means you can save all your data without designing it carefully or knowing what questions you may have to answer in the future. A variety of ways to analyze your data can be used to generate insights, such as: SQL queries, full-text search, big data analysis, and machine learning.
The main difference between the two is that in the data warehouse the data schema is predefined. That is, there is a plan for the data when it enters the database. This doesn’t necessarily happen in data lakes. Data lakes can contain both structured and unstructured data, and there are no predefined schemas. Data warehouses process structured data and have predefined schemas for stored data.
Just look at the concept of the camp versus the concept of the lake. Generally the lake is liquid, amorphous, agile, largely unstructured and connected by rivers, streams and other sources of unfiltered water. A warehouse, on the other hand, is a man-made structure with shelves and walkways and special places for items inside. Goods selected from certain sources are stored in the warehouse. The camps were built beforehand and the lakes were not. This basic conceptual difference manifests itself in several ways, including:
- Technologies Typically Used for Hosting Data: Data warehouses are typically relational databases hosted on servers or corporate mainframe clouds, whereas data lakes are typically hosted in a Hadoop environment or similar large data storage.
- Data sources: Data stored in the warehouse is retrieved from various online transaction processing applications to support business analysis queries and data for specific internal business groups such as sales or inventory teams. Data lakes typically receive relational and non-relational data from IoT devices, social media, mobile applications, and enterprise applications.
- User data warehouses are useful when large amounts of operating system data need to be easily accessible for analysis. Data lakes are more useful when a business needs a large data warehouse, but they are not intended for everyone and can be schema as they are accessed.
- User: Data warehouses are useful when large amounts of operating system data need to be easily accessible for analysis. Data lakes are more useful when a business needs a large data warehouse, but they are not intended for everyone and can be schema as they are accessed.
- Because the data in pools is often unprocessed and may come from sources outside the company’s operating systems, they are not suitable for the average business analyst. Data sets are more suitable for data researchers, however, because a level of knowledge is required to sort through large amounts of unexplored data and easily extract meaning from them.
- Data Quality: In Data warehouses, it is usually assumed that high quality data is the central version of true because it contains processed data. The data in the data lake is less reliable because it can come from any source in any country. It may or may not be curated depending on the source.
- Processing: Data warehouse schema in write mode. This means that it is present when data is entered into the warehouse. Data lake schemas are readable, meaning they don’t exist until the data is available and someone decides to use them for something.
- Productivity / Cost: Data warehouses are typically more expensive for large amounts of data, but the tradeoffs are faster query results, reliability and better performance. Data lakes are inexpensive, but query yields improve as the concepts and technology around them develop.
- Skills: Data lake is very agile; They can be configured and reconfigured as needed. Smaller data warehouse.
- Security: Data warehouses are usually more secure than data lakes because warehouses take longer because security concepts and methods are mature.
Due to their differences and the fact that data lakes are a newer and evolving concept, organizations can use data warehouses and data lakes in hybrid applications. This can consist of customizing adding a new data source or creating an archive repository to handle dumping data from the main data warehouse. Oftentimes, data pools are more of an addition or evolution to the company’s current data management structure than a replacement.
Data Lake Architecture
The physical architecture of the data lake can vary because the data lake is a strategy that can be applied to several technologies. For example, the physical data lake architecture with Hadoop can be different from the data lake architecture with Amazon Simple Storage Service (Amazon S3).
However, there are three basic principles that differentiate data pools from other large data storage methods and which form the basic architecture of a data lake. They are:
- No data rejected. All data is loaded and stored from multiple sources.
- Data is stored in the unaltered or almost unmodified state it received from the source.
- Data were transformed and entered into a schema based on the requirements for analysis.
Although most of the data is unstructured and not intended to answer specific questions, it is still necessary to organize this data for this to happen in the future. Regardless of the technology used to locate an enterprise data pool, there are several functions that should be included to ensure that a functional, robust data pool and a large unstructured data store is not wasted. They include:
- Data classification taxonomy, which can include data types, content, usage scenarios, and potential user groups.
- File hierarchy with naming conventions.
- A data profiling tool that provides insight into how to classify data objects and troubleshoot data quality issues.
- Standard data access process for keeping track of which members of the organization have access to data.
- Browse the data catalog.
- Data protection, including data masking, data encryption, and automatic monitoring to generate alerts when unauthorized persons access data.
- Knowledge of data among employees, including understanding of data management and correct data management, training on data lake navigation, and understanding of high data quality and correct data usage.
The basic concept of data lakes
The following are important data lake concepts that must be understood to fully understand the data lake architecture.
By collecting data, connectors can receive data from multiple data sources and load it into the data pond. It supports:
- All types of structured, semi-structured and unstructured data.
- Multiple ingestions as a single batch real-time payload.
- Many types of data sources such as databases, web servers, email, IoT and FTP.
Data storage must be scalable, provide inexpensive storage, and allow fast access to data exploration. It must support different data formats.
Data management is the process of managing the availability, usability, security, and integrity of data used within an organization.
Security must be implemented at every layer of the data lake. It begins with storage, excavation, and consumption. The main requirement is to terminate access for unauthorized users. It should support a variety of data access tools with easy to navigate graphical interfaces and dashboards. Authentication, billing, authorization and privacy are some of the important features of data lake security.
Data quality is an integral part of the data lake architecture. This data is used for accurate business value. Extracting insights from poor quality data results in poor quality insights.
Data discovery is another important step before you start preparing any data or analysis. At this stage, the tagging technique is used to express understanding of the data by organizing and interpreting the data absorbed in the data pond.
Data review helps evaluate risk and compliance. The two main tasks for data review are tracking changes in key records.
- Track changes in key elements of the data set
- Record how / when / and who changed this item.
This component deals with the origin of the data. It’s primarily about where it moves from time to time and what happens to it. Simplifies error correction when analyzing data from origin to destination.
This is the first stage of data analysis. Before starting any data collection, it is important to identify the correct data set. All of the components provided must work together to play an important role in building a lake with data that will make the environment easy to develop.
Best practices for implementing a data lake
- The architectural components, their interactions, and the products identified must support natural data types
- The data lake design should be based on what’s available, not what’s needed. The schema and data requirements are determined only after the requirements
- The design should be guided by a one-way component built into the service API.
- Data discovery, retrieval, storage, management, qualification, transformation and visualization must be managed independently.
- The data lake architecture must be industry specific. It is necessary to ensure that the functionality required for that domain is an integral part of the design
- It is important to upload newly discovered data sources more quickly
- Data Lake helps personalized management achieve maximum value
- Data lakes must support existing techniques and methods for managing corporate data.
Challenges in building a data lake
- In Data Lake, the amount of data is bigger, so the process has to rely more on software management
- It is difficult to handle scarce, incomplete, and unstable data
- A wider range of data and sources requires more data management and maintenance
Advantages of the data lake
Data pools offer several advantages, including:
- The ability of data developers and researchers to easily configure data models, applications, or queries on the go. The data lake is very agile.
- In theory, data lakes are easier to access. Since there is no inherent structure, any user can technically access the data in the data lake, although the deployment of large amounts of unstructured data may deter less qualified users.
- Data lakes support users at various levels of investment. Users who want to return to the source for more information, those who want to answer a whole new data question, and those who just need a daily report.
- Data lakes are inexpensive to implement because most of the technologies used to manage them are open source. It is an inexpensive hardware.
- The development of labor-intensive schemes and data cleaning is postponed until the company determines clear business data requirements.
- Agility enables a variety of different analytical methods to interpret data, including big data analysis, real-time analysis, machine learning, and SQL queries.
- Scalable due to lack of structure.
The above mentioned Data lakes and data warehouses are different tools used for different purposes. If you already have a data warehouse, you can deploy a data lake with it to overcome some of the limitations that exist with data storage. To determine which a data lake or data warehouse is the most suitable for your company needs, you must start with the goals you want to achieve and use data storage to achieve your goals.