Ever since Big Data came to the limelight, data lakes and data warehouses jumped into the scene. While both are data lakes and data warehouses are storehouses for Big Data, they are not the same. The only similarity between a data lake and a data warehouse is that they are used to store data. To understand these storage repositories’ unique purposes, it is essential to identify the difference between data lake and data warehouse.
A data warehouse is a storage repository for large volumes of data collected from multiple sources. Before data is fed into a data warehouse, you must clearly define its use case. It usually contains both historical and present data in a structured format. The data stored in a data warehouse is used by businesses to create annual and quarterly reports to measure business performance.
A data lake is a pool of raw data (data in its natural state) that flows like streams from data sources into the lake. Data lakes accept all data types, irrespective of whether or not it is structured or unstructured. First, the data is stored at the leaf level in an untransformed state, after which it is transformed, and schema is applied to fulfill the needs of analysis. Users can access the lake to dive in and take data samples to fuel business innovation.
One of the biggest differences between data lake and data warehouse is the way they store data. While data lakes store raw and unprocessed data, data warehouses store organized and processed data. This is primarily the reason why data lakes require a larger storage capacity. By storing processed and structured data, data warehouses save valuable storage space and cut down costs.
The most significant benefit of data warehouses is that since they store processed data having a defined use case, businesses can readily use it for their organizational needs. Raw data also has a clear advantage – unprocessed data is highly flexible, making it ideal for ML tasks. However, since data lakes have no strict data quality and data governance measures, they can fast turn into data swamps.
A data lake is characterized by minimal organization and filtration. Data can flow into a data lake from any source. Generally, individual data elements in a data lake don’t have a defined or fixed purpose. On the other hand, data warehouses store processed data that will be used for specific business purposes. Thus, data warehouses never store data that has no use within an organization.
The ease of accessing data from a data repository depends on the storage structure as a whole. Since data lakes have no set structure or strict limitations, you can easily access and modify the data as and when required. Contrary to this, the architecture of a data warehouse is more structured. This is beneficial since processed data is easy to interpret and understand.
Raw and unstructured data is pretty tricky to manage, analyze, and interpret. Data scientists and data analysts typically deal with raw data to extract meaningful patterns from it and transform them into actionable business strategies. Thus, data lakes require much more skilled and expert users who know the nitty-gritty of dealing with raw data.
On the other hand, you can easily visualize processed data in the form of charts, tables, graphs, spreadsheets, etc. This is why data warehouses have a more extensive user base – anyone having the basic knowledge of business data can work with data warehouses.
Perhaps the biggest issue of data warehouses is that they are not flexible or adaptable. It takes a significant amount of time, resources, and effort to modify a data warehouse’s structure, mainly because the data loading process is complicated. However, as the data always remains in its raw form in a data lake, anyone can access it anytime. You can explore and experiment with the raw data in any way you desire, without any restrictions.
#data science #data lake
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
In this sense, data is all information that is to be saved and later reused in various contexts. These can be date and time values, texts, addresses, numbers, but also pictures. The data should be able to be evaluated and processed later.
The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.
#data-warehouse #data-lake #cloud-data-warehouse #what-is-aws-data-lake #data-science #data-analytics #database #big-data #web-monetization
We recently wrote an article debunking common myths about data lake architectures, data lake definitions, and data lake analytics. It is called "What is a Data Lake_? Get A Leg Up Avoiding The Biggest Myths." _In that article, we framed the current conversation about data lakes and how they fit within enterprise data strategies. This topic has historically been confusing and opaque for those wanting to get value from a data lake due to conflicting advice from consultants and vendors.
One area that can be particularly confusing is the perception that lakes are only for “big data.” If you spend any time reading materials on lakes, you would think there is only one type and it would look like the Capsian Sea (it’s a lake despite “sea” in the name). People describe data lakes as massive, all-encompassing entities, designed to hold all knowledge. The good news is that lakes are not just for “big data” and you have more opportunities than ever to have them be part of your data stack.
Just as they do in nature, lakes come in all different shapes and sizes. Each has a natural state, often reflecting ecosystems of data, just like those in nature reflect ecosystems of fish, birds, or other organisms.
Unfortunately, the “big data” angle gives the impression that lakes are only for “Caspian” scale data endeavors. This certainly makes the use of data lakes intimidating. As a result, describing things in such massive terms makes the concept of a lake inaccessible to those who can benefit from them on a smaller scale. Here are a few data lake examples;
We recently worked with a customer to create a “Domain” type lake. This lake would hold Adobe event data to an AWS to support an enterprise Oracle Cloud environment. Why AWS to Oracle? It was an efficient and cost-effective data consumption pattern for the customer Oracle BI environment, especially considering the agility and economics of using an AWS lake and Athena as the on-demand query service for lake content.
By design, all types of lakes should embrace an abstraction that minimizes risk and affords you greater flexibility. Also, they should be structured for easy consumption independent of their size. This ensures a lake used by a data scientist or business user or analyst all have an environment structured for easy data consumption.
Being a successful early adopter means taking a business value approach rather than a technology one. Here are a few tips as you think about how to get started:
#big data #data lake #data lakes #data lake architecture #data lake solutions #data analysis
As data mesh advocates come to suggest that the data mesh should replace the monolithic, centralized data lake, I wanted to check in with Dipti Borkar, co-founder and Chief Product Officer at Ahana. Dipti has been a tremendous resource for me over the years as she has held leadership positions at Couchbase, Kinetica, and Alluxio.
According to Dipti, while data lakes and data mesh both have use cases they work well for, data mesh can’t replace the data lake unless all data sources are created equal — and for many, that’s not the case.
All data sources are not equal. There are different dimensions of data:
Each data source has its purpose. Some are built for fast access for small amounts of data, some are meant for real transactions, some are meant for data that applications need, and some are meant for getting insights on large amounts of data.
Things changed when AWS commoditized the storage layer with the AWS S3 object-store 15 years ago. Given the ubiquity and affordability of S3 and other cloud storage, companies are moving most of this data to cloud object stores and building data lakes, where it can be analyzed in many different ways.
Because of the low cost, enterprises can store all of their data — enterprise, third-party, IoT, and streaming — into an S3 data lake. However, the data cannot be processed there. You need engines on top like Hive, Presto, and Spark to process it. Hadoop tried to do this with limited success. Presto and Spark have solved the SQL in S3 query problem.
#big data #big data analytics #data lake #data lake and data mesh #data lake #data mesh
In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.
These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.
#cloud #data analytics #business intelligence #hybrid cloud #data warehouse #data storage #data management solutions #master data management #data warehouse architecture #data warehouses