The exact difference between the Data Warehouse and AWS Data Lake.
Let me demonstrate what AWS Data Lake is all about and depict the reason for it’s popularity.
In the world of Amazon Web Service (AWS), Amazon S3 is an amazing object container. Like any bucket, you can put content in it in a neat and orderly fashion, or you can just dump it in. But no matter how the data gets there, once it’s there, you need a way to organize it in a meaningful way so you can find it when you need it. This is where data lakes come in.
AWS Data Lake
A data lake is a centralized repository that allows you to store structured, semi-structured, and unstructured data at any scale.
A data lake is an architectural concept that helps you manage multiple data types from multiple sources, both structured and unstructured, through a single set of tools. A data lake takes Amazon S3 buckets and organizes them by categorizing the data inside the buckets. It doesn’t matter how the data got there or what kind it is, you can store both structured and unstructured data effectively in an Amazon S3 data lake AWS offers a set of tools to manage the entire Data Lake without treating each bucket as separate, unassociated objects
On-Premises Data Movement
Data lakes allow you to import any amount of data. Data is collected from multiple sources and moved into the data lake in it’s original format. The process allows you to scale the data of any size, while saving time from defining data structures, schemas and transformation.
Real-time Data Movement
Data lakes allow you to import any amount of data that can come in real time. Data can be collected from multiple stream data sources and moved into the data lakes in its original format.
Data lakes enables organizations to generate different types of insights including reporting on historical data and implementing machine learning where models are built to forecast likely outcomes and suggest a range of prescribed actions to achieve the optimal result.
Data lakes allow various roles in the organization, such as Data Scientists, Data Developers and Business Analysts, to access data with their choice of analytic tools and frameworks.
This includes open source frameworks such as Hadoop, Presto, and Apache Spark and commercial offerings from data warehouse and BI vendors.
Data lakes allow you to run analytics without the need to move your data to a separate analytics system.
Many businesses end up grouping data together into numerous storage locations called silos. These silos are rarely managed and maintained by the same team, which can be problematic.
Inconsistencies in the way data was written, collected, aggregated, or filtered can cause problems when it is compared or combined for processing and analysis.
For example, one team may use the address field to store both the street number and street name, while another team might use separate fields for street number and street name. When these datasets are combined, there is now an inconsistency in the way the address is stored, and it will make analysis very difficult.
But by using Data Lakes, you can break down data silos (a repository that contains raw data that is accessible by one department but isolated from the rest of that organization) and bring data into a single, central repository that is managed by a single team. That gives you a single, consistent source of truth. Because data can be stored in its raw format, you don’t need to convert it, aggregate it, or filter it before you store it. Instead, you can leave that pre-processing to the system that processes it, rather than the system that stores it.
In other words, you don’t have to transform the data to make it usable. You keep the data in its original form, however it got there, however it was written. When you’re talking exabytes of data, you can’t afford to pre-process this data in every conceivable way it may need to be presented in a useful state.
Let’s talk about having a single source of truth. When we talk about truth in relation to data, we mean the trustworthiness of the data. Is it what it should be? Has it been altered? Can we validate the chain of custody? When creating a single source of truth, we’re creating a dataset, in this case the data lake, which can be used for all processing and analytics. The bonus is that we know it to be consistent and reliable. It’s trustworthy.
Amazon S3 Data Lakes provide a single storage backbone for a solution meeting these requirements and tools for analyzing the data without requiring movement.
As the volume of data has increased, so have the options for storing data. Traditional storage methods such as data warehouses are still very popular and relevant. However, data lakes have become more popular recently. These new options can confuse businesses that are trying to be financially wise and technically relevant.
So which is better: data warehouses or data lakes? Neither and both. They are different solutions that can be used together to maintain existing data warehouses while taking full advantage of the benefits of data lakes.
A data warehouse is a central repository of information coming from one or more data sources. Data flows into a data warehouse from transactional systems, relational databases, and other sources. These data sources can include structured, semistructured, and unstructured data.
These data sources are transformed into structured data before they are stored in the data warehouse. Data is stored within the data warehouse using a schema. A schema defines how data is stored within tables, columns, and rows. The schema enforces constraints on the data to ensure integrity of the data. The transformation process often involves the steps required to make the source data conform to the schema.
Following the first successful ingestion of data into the data warehouse, the process of ingesting and transforming the data can continue at a regular cadence. Business analysts, data scientists, and decision makers access the data through business intelligence (BI) tools, SQL clients, and other analytics applications. Businesses use reports, dashboards, and analytics tools to extract insights from their data, monitor business performance, and support decision making. These reports, dashboards, and analytics tools are powered by data warehouses, which store data efficiently to minimize I/O and deliver query results at blazing speeds to hundreds and thousands of users concurrently.
Analyzing a Data Warehouse
For analysis to be most effective, it should be performed on data that has been processed and cleansed. This often means implementing an ETL operation to collect, cleanse, and transform the data. This data is then placed in a data warehouse. It is very common for data from many different parts of the organization to be combined into a single data warehouse.
Analyzing a Data Lake
Data lakes provide customers a means for including unstructured and semistructured data in their analytics. Analytic queries can be run over cataloged data within a data lake. This extends the reach of analytics beyond the confines of a single data warehouse.
Businesses can securely store data coming from applications and devices in its native format, with high availability, durability, at low cost, and at any scale. Businesses can easily access and analyze data in a variety of ways using the tools and frameworks of their choice in a high-performance, cost-effective way without having to move large amounts of data between storage and analytics systems.
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