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 mazon eb ervice ( ), 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. A W S AWS 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 , and , through a single set of tools. A data lake takes Amazon S3 buckets and 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 manage multiple data types from multiple sources both structured unstructured organizes them by categorizing the data On-Premises Data Movement Data lakes allow you to . 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 , while from defining data structures, schemas and transformation. import any amount of data any size saving time Real-time Data Movement Data lakes allow you to import . Data can be collected from multiple stream data sources and moved into the data lakes in its original format. any amount of data that can come in real time Machine Learning Data lakes enables organizations to including reporting on and implementing where are built to forecast likely outcomes and suggest a range of prescribed actions to achieve the optimal result. generate different types of insights historical data machine learning models Analytics 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 such as Hadoop, Presto, and Apache Spark and commercial offerings from data warehouse and BI vendors. open source frameworks Data lakes allow you to run analytics system. without the need to move your data to a separate analytics Benefits of a data lake on AWS Are a solution. You can durably store a nearly unlimited amount of data using Amazon S3. cost-effective data storage Implement industry-leading . AWS uses stringent data security, compliance, privacy, and protection mechanisms. security and compliance Allow you to take advantage of to ingest data into your data lake. many different data collection and ingestion tools Help you to simply and efficiently. Use AWS Glue to understand the data within your data lake, prepare it, and load it reliably into data stores. Once AWS Glue catalogs your data, it is immediately searchable, can be queried, and is available for ETL processing. categorize and manage your data Help you turn data into . Harness the power of purpose-built analytic services for a wide range of use cases, such as interactive analysis, data processing using Apache Spark and Apache Hadoop, data warehousing, real-time analytics, operational analytics, dashboards, and visualizations. meaningful insights Business Problem 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. AWS Solution But by using Data Lakes, you can (a repository that contains raw data that is accessible by one department but isolated from the rest of that organization) and that is managed by a single team. That gives you a single, . 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. break down data silos bring data into a single, central repository consistent source of truth In other words, you to make it usable. You , 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. don’t have to transform the data keep the data in its original form Let’s talk about having a . When we talk about truth in relation to data, we mean the . 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 bonus is that we know it to be and . It’s trustworthy. single source of truth trustworthiness of the data the data lake, which can be used for all processing and analytics consistent reliable Amazon S3 for a solution meeting these requirements and tools for analyzing the data without requiring movement. Data Lakes provide a single storage backbone Why Data Lake is Popular? As the volume of data has increased, so have the options for storing data. Traditional storage methods such as However, . These new options can confuse businesses that are trying to be and . data warehouses are still very popular and relevant. data lakes have become more popular recently financially wise technically relevant : data warehouses or data lakes? . They are different solutions that can be used together while . So which is better Neither and both to maintain existing data warehouses taking full advantage of the benefits of data lakes Data Warehouse A data warehouse is a 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. central repository of information . Data is within the data warehouse . A schema defines how data is stored within tables, columns, and rows. The to ensure integrity of the data. The transformation process often involves the steps required to make the source data conform to the schema. These data sources are transformed into structured data before they are stored in the data warehouse stored using a schema schema enforces constraints on the data Following the first successful of data into the data warehouse, the process of ingesting and 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 , , and tools to extract insights from their data, , and . 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. ingestion transforming reports dashboards analytics monitor business performance support decision making Comparison of Data Warehouse and Data Lake 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 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. implementing an ETL operation to collect, cleanse, and transform the data. Analyzing a Data Lake Data lakes provide customers a means for including unstructured and semistructured data in their analytics. . This beyond the confines of a single data warehouse. Analytic queries can be run over cataloged data within a data lake extends the reach of analytics Businesses can , with high , , at and at . 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. securely store data coming from applications and devices in its native format availability durability low cost, any scale I hope the above content is knowledgeable and would have given you a glance about the topic. Do follow me on & to get updates regarding all my blogs. If you really enjoy this post, then do show your love by banging the because learning has no limits. Medium LinkedIn Claps Button below Thank you for reading…!!
