What is a data lake? Definition, benefits, architecture and best practices

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What is Data Lake?

Data Lake is defined as a centralized and scalable storage repository that contains large amounts of raw large data from multiple sources and systems in its original format.

To understand what a data lake is, consider a data lake as a real lake, where water is the raw data that flows from multiple sources of data capture and then can be used for internal and consumer-content purposes. . This data is much wider than a warehouse, which would be like a home tank, which stores clean water (structured data) but not just for the use of a particular home and nothing else.

Data leaks can be executed using in-house built-in tools or third-party vendor software and services. According to Markets & Markets, the global data lac software and services market is expected to grow from $ 7.9 billion in 2019 to $ 20.1 billion in 2024. A number of vendors, including Databrix, AWS, Dremio, Qubole and MongoDB, are expected to drive this growth. . Many organizations have also started offering so-called lakehouses combining the advantages of both data lakes and warehouses through a single product.

Data Lake first works on the concept of load and then uses it, which means that the data stored in the repository does not need to be used immediately for a specific purpose. It can be dumped as it is and used together (or in parts) to meet the needs of the business at a later stage. With a wide variety and amount of data stored, this flexibility makes Data Lake ideal for data experiments as well as advanced learning and analytics applications in machine learning.

Data Lake Vs. Data warehouse

Unlike data warehouses, which store only processed structured data (arranged in rows and columns) for certain predefined business intelligence / reporting applications, Data Lex brings the ability to store everything without any limits. These can be structured data, semi-structured data or even unstructured data such as images (.jpg) and video (.mp4).

Key benefits and challenges

Benefits of Data Lake for Enterprises

  • Extended data-type for storage: Data Lex brings the ability to store all data types, including those important to perform advanced forms of analysis, organizations can benefit from them to identify opportunities and actionable insights that can help improve operational efficiency, increase revenue, save money and reduce risk. .
  • Revenue growth from Extended Data Analytics: According to the Aberdeen survey, organizations implementing Data Lake outperformed similar companies by more than 9% in terms of organic revenue growth. These companies were able to perform new types of analytics on previously unused data stored in Data Lake – log files, data from click-streams, social media and Internet-connected devices.
  • Unified data from Silos: Data Lex can also centralize information from different departmental silos, mainframes and legacy systems, offloading their individual capabilities, preventing issues like data duplication and giving users a 360-degree view. At the same time, they lower the cost of storing data for future use.
  • Advanced data capture including IoT: An organization can implement a data leak to inject data from multiple sources, including sensors of IoT equipment into factories and warehouses. These sources for integrated data lakes can be internal and / or customer-facing. Customer-facing data helps marketing, sales and account management teams run omni-channel campaigns using the most up-to-date and integrated information available to each customer, while internal data is used for holistic employee and finance management strategies.

Data Lake Challenges

Over the years, Cloud Data Lake and warehousing architectures have helped enterprises scale their data management efforts by reducing costs. However, the current set-up has some challenges, such as:

  • Lack of compatibility with warehouses: Companies can often find it difficult to keep their data leaks and data warehouse architectures consistent. Not only is it expensive, but teams also need to use continuous data engineering tactics for ETL / ELT data between the two systems. Each step can present failures and unwanted errors that affect the quality of the data as a whole.
  • Vendor lock-in: Transferring large amounts of data to a centralized EDW not only because of the time and resources required for companies to carry out such work but also because of this architecture creates a closed-loop which makes vendor lock-in very challenging.
  • Data Governance: While the data in DataLake is mostly in a different file-based format, the data warehouse is mostly in a database format, and it adds complexity in terms of lineage management between data governance and the two storage types.
  • Copies of data and related costs: Data leaks and data warehouses available in the data warehouse lead to the extent of data copying and have associated costs. Moreover, commercial warehouse data in proprietary format increases the cost of transferred data. Data Lake House addresses these typical limitations of Data Lake, as well as data warehouse architecture, combining the best elements of both Data Warehouse and Data Lake to provide significant value to organizations.0

Architecture of Data Lake: 5 main components

Data Lake uses a flat architecture and can have multiple levels depending on technical and business requirements. No two data lakes are built exactly alike. However, there are some major zones through which general data flows – ingestion zone, landing zone, processing zone, refined data zone and consumption zone.

1. Data ingestion

This component, as the name suggests, connects the data leak to external and unrelated sources – such as social media platforms and wearable devices – and loads raw structured, semi-structured and unstructured data into the platform. Ingestion is done in batches or in real-time, but it should be noted that the user may need different techniques to inject different types of data.

Currently, all major cloud storage providers offer solutions for low latency data ingestion. These include Amazon S3, Amazon Glue, Amazon Kinesis, Amazon Athena, Google Dataflow, Google BigQuery, Azure Data Factory, Azure Databricks and Azure Functions.

2. Data landing

Once the ingestion is complete, all data is stored as metadata tags and unique identifiers in the landing zone. According to Gartner, this is usually the largest zone in the data lake today (in terms of volume) and serves as an ever-available repository of detailed source data, which can be used for analytical and operational use-cases whenever and wherever. The need arises. The presence of raw source data also makes this zone an early playground for data scientists and analysts who experiment to define the purpose of the data.

3. Data processing

When the purpose (s) of the data are known, copies of it move from the landing to the processing stage, where refinement, optimization, aggregation, and quality standardization take place by imposing some schema. This zone makes data analysis suitable for a variety of business use cases and reporting needs.

Significantly, copies of the data are moved to this stage to ensure that the original arrival status of the data is preserved in the landing zone for future use. For example, if new business questions or usage cases arise, source data can be explored and reused differently, without prejudice to previous optimizations.

4. Pure data zone

When data is processed, it goes into a pure data zone, where data scientists and analysts set up their own data science and staging zones to serve as a sandbox for specific analytical projects. Here, they control the processing of data to reuse raw data into structures and quality states that can enable analysis or feature engineering.

5. Access zone

Usage area is the last phase of normal data flow in data lake architecture. At this level, analytical consumer tools and, through SQL and non-SQL query capabilities, the results of analytical projects and business insights are made available to the target users, be they technical decision makers or business analysts.

Top 6 Best Practices for Effective and Secure Data Lake in 2022

1. Identify data targets

To prevent your data leak from becoming a data swamp, it is recommended to identify your organization’s data goals – business results – and appoint an internal or external data curator who can evaluate new sources / datasets and manage what goes into the data leak. It depends on the goal. Clarification of what type of data to collect can help the organization overcome the problem of data redundancy, which often overwhelms analytics.

2. Document incoming data

All incoming data must be documented as it is dumped into the pond. Documentation typically takes the form of technical metadata and business metadata, although new forms of documentation are also emerging. Without proper documentation, the data leaks into a data swamp that is difficult to use, manage, optimize, and trust. Users fail to find the required data.

3. Maintain fast ingestion time

The ingestion process should run as fast as possible. Eliminating previous data improvements and conversions and adopting new data integration methods for pipelining and orchestration increases ingestion speed. This will help make the data available as soon as possible after the data has been created or updated so that some form of reporting and analytics can work on it.

4. Process data in moderation

The main goal of Data Lake is to provide detailed source data for data research, discovery and analysis. If an enterprise processes ingested data with heavy aggregation, standardization, and conversion, many of the details captured with the original data will be lost, defeating the whole purpose of the data leak. Therefore, the enterprise should make sure to implement data quality measures in moderation while processing.

5. Focus on the subzone

Individual data zones can be arranged in the lake by creating internal subzones. For example, a landing zone can have two or more subzones depending on the data source (batch / streaming). Similarly, the data science zone under the refined datasets layer may include subzones for analytics sandboxes, data laboratories, test datasets, learning data and training, while staging zones for data warehousing may contain subzones that target data structures or subject areas in target data. doing. Warehouse (e.g., rows for dimension, metrics and reporting tables and so on).

6. Prioritize data security

Security must be maintained in all zones of the data lake from landing to use. To ensure this, connect with your vendors and see what they’re doing in these four areas – user authentication, user authentication, data-in-motion encryption, and data-at-rest encryption. With these elements, an enterprise can keep its data leaks active and secure, without the risk of external or internal leaks (due to incorrectly configured permissions and other factors).

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