Until analytics applications use it, a data lake is a type of storage facility that keeps large amounts of unprocessed raw data in its original format. A data lake stores data in a flat design, typically in files or object storage, as opposed to a traditional data warehouse, which stores data in hierarchical dimensions and tables. The options available to users for managing, storing, and using their data have increased.
What is the requirement for a data lake for the organization?Â
Data lakes are frequently used to store big data sets that mix structured, unstructured, and semi-structured data. These situations are not well suited for the relational databases that the majority of data warehouses are built on. Because they need a predetermined data schema, relational systems typically can only store structured transaction data. Data lakes don’t require any upfront definition and allow a variety of schemas. They can now manage various kinds of data in distinct forms as a result.
Data lakes are therefore a crucial part of many firms’ data architectures. They are usually used by businesses as a platform for big data analytics and other data science applications that include sophisticated analytics methods like data mining, predictive modeling, and machine learning that call for massive amounts of data.
Data scientists and analysts can locate, prepare, and assess crucial data all in one location with the aid of a data lake. That procedure is more difficult without one. It’s also becoming more challenging for businesses to fully utilize their data assets to support smarter business decisions and strategies.
What is the difference between Data Warehouse and Data Lake?
| Basis | Data Lake | Data Warehouse |
| Data Storing | Data storage without a defined purpose. Both relational and non-relational data are IoT devices, websites, mobile applications, social media, and business applications. | The storage data in relational systems are distinct from operational databases, line-of-business applications, and transactional systems. |
| Schema writing | In the case of the data lake, schemas is written as schema-on-read at the moment of analysis. | While in the case of the data warehouse the schema design before the DW was implemented (schema-on-write) |
| Data type | Data remains raw until it is needed for a query. | The processing of data is ongoing, and it is simple to use for querying. |
| Analysis | Data discovery and profiling, predictive analysis, and machine learning | Business Intelligence and visualization, batch reporting |
| Used by | Data scientists and engineers who prefer to analyze data in its raw form to gain fresh, original business insights frequently use data from a “data lake,” which contains a large volume of unstructured data. | Managers as well as additional business-end users typically access data from a data warehouse because the data has already been structured to provide answers to pre-determined questions for analysis. This allows managers and business-end users to gain insights from business KPIs. |
| Cost | Compared to a data warehouse, storage costs in a data lake are relatively low. Operational costs are decreased because managing data lakes takes less time. | Data warehouses are more expensive and take more work to manage than data lakes, which drives up operational costs. |
Why create a data lake?
To store data from data sources, the Data Lake offers a sizable storage pool. Here are the top 4 reasons to create a data lake:
1) ConsolidationÂ
The company’s data is spread across several frequently used platforms. The data may be located in marketing applications, CRM platforms, ERP systems, etc. It has improved the organization of the data on each company’s platform. However, this is not always the case because you need all the data in one place when you’re analyzing the funnel and attribution data.
The ideal way to gather all the data from various sources in one location is to use a data lake. The Data Lake Architecture makes it easier for businesses to understand their data thoroughly and derive insights from it.
2) Full Query Access
Transactional API access to the data is offered by the vast majority of enterprise platforms that businesses use to carry out their daily operations. These APIs are not made to meet the needs of reporting tools, which results in a lack of access to data. When data is stored in data lakes, full access to it is provided, making it possible for BI tools to pull data whenever necessary.
Data can be loaded into the Data Lake quickly, reliably, and flexibly using the ELT process, which can then be used with other tools.Â
3) PerformanceÂ
Data sources are frequently the production systems that can’t process queries more quickly. The effectiveness of the application it is powering may be impacted. Faster query speeds are needed for data aggregation, and transactional databases are not thought to be the best option for this. Rapid query processing is a feature of the Data Lake Architecture. Users can run ad hoc analytical queries without relying on the production environment thanks to it. Scaling up and down is made simpler and faster thanks to the data lake.
4) Progress
Before moving on to the next stage, gathering all of the data in one location is essential because doing so makes using BI tools easier. Data lakes enable you to create data that is less repetitive, cleaner, and error-free.
Top Data Lake platforms
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Lyftrondata
Lyftrondata is one of the best platforms that support the data lake with a governed delta lake that serves as an anti-corruption layer, permits nested tagging by resource groups, and ensures effective and efficient use of data. Delta Lake is yet another registered data source that Lyftrondata accepts. Lyftrondata, the gateway to the data Lake, has tags for the validated tables from the delta Lake. Views are any further interpretations of the source data. Prior to being loaded into Data Lake, new data sources are first evaluated in Lyftrondata.
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Snowflake
Snowflake‘s data lake solution assists in the breakdown of silos so that businesses can enhance their strategies. It is safe, dependable, and simple to access. A central platform for all data, safe collaboration, and quick querying are just a few of Snowflake’s data lake’s top features.
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Oracle
Businesses can create data lakes using Oracle‘s Big Data Service to manage the influx of information required to drive their business decisions. Users will have access to an automated, all-inclusive Hadoop data lake platform through the Big Data Service, which is built on Cloudera Enterprise.Â
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Google BigQuery
Another major player in technology offering customers data lake solutions is Google. Businesses can securely and affordably analyze any data using the data lake offered by Google BigQuery. It can process large amounts of data as well as the various processing tasks performed by IT professionals. Companies can easily lift and shift their data to Google BigQuery without having to recreate their on-premise data lakes in the cloud.Â
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AWS Amazon Web Services
Many necessary resources, including tools and services, are available through the AWS Cloud to help businesses create a data lake that works for them. The AWS data lake solution is widely used, cost-effective, and user-friendly. It utilizes the security, dependability, adaptability, and scalability that Amazon S3 object storage offers its users.Â
Conclusion
Reading this blog will help you understand Data Lake thoroughly. You will learn what a “data lake” is, why organizations need one, the difference between a “data lake” and a “data warehouse,”, why you should build one, and a list of best Data Lake platforms. Hope reading my blog will help you gain some value and more related information just click here.
