What is the best data warehouse tool?

Why would you choose that one?

Content Manager at PeerSpot (formerly IT Central Station)
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Top 5
May 17, 2022

When I compared various data warehouse tools and solutions, I found Snowflake’s software as a service (SaaS) platform and Oracle Exadata to be the most effective data warehouse solutions currently available on the market.

One of the things that I initially noticed about Snowflake’s software as a service (SaaS) platform was how it made my operations more efficient by enabling me to search for and find relevant data in a more efficient way than had previously been possible. Snowflake allows me to create a custom storage unit for all of my critical data. Part of this customization includes the ability to make any and all data searchable.

As soon as I started to use it, it began to show its value. All of the data that I have stored in Snowflake’s virtual warehouse becomes easy for me to locate. Instead of spending long periods of time seeking the particular piece of data that I need, all I have to do is to type in a search term. This will immediately call up the information that I want to find. This aspect of the solution makes it immensely valuable. It enables me to save time that I can then devote to other critical tasks.

A major advantage that Snowflake offers me is that it gives me the ability to perform a number of different functions with a single solution. It is a highly flexible solution and allows me to store organized processed data, centrally store raw data that has yet to be processed, process data through data engineering, examine data using data science, develop data applications, and securely share and take in real-time or shared data. It empowers me to make my data really and truly my own. I am able to make full use of my data and shape it in whatever way my needs dictate.

Two aspects of the Oracle Exadata Database Machine that I really appreciate are its scalability and its ease of use. This solution makes it so that I can expand my digital warehouses virtually limitlessly. If I needed to, the Oracle Exadata Database Machine would enable me to scale my data warehouses to hold 31 petabytes of data. I can easily meet my data needs without having to worry about running out space for my data. Additionally, every component of this tool, including its database servers, storage servers, and network, are all ready to use straight out of the box. Everything about this solution is pre-configured, pre-tuned and pre-tested before we ever received it from Oracle. All of the components work in perfect harmony without outside intervention. This means that I don’t have to struggle to deploy it or do very much to get all of the features to work in tandem.

This solution also offers me the ability to easily move workloads from a data center to the cloud. I am able to migrate my data without having to worry about availability, scaling, or performance. My workloads lose nothing in the transfer. Additionally, the same database options that are available to me on my physical systems are available on the cloud. This allows me to continue using the solution the same way that I had been using it up to this point.

Ultimately, either of these two solutions will empower you to take full control of every stage of your data and its lifecycle from its initial storage to its final use.

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What are the relations between them? What are their use cases?
2 out of 7 answers
Consulting Practice Partner - Data, Analytics & AI at FH
Oct 10, 2021
Hi @Evgeny Belenky ​ - great question.  Here is the best answer crafted by Talend  Data Lake Data Warehouse Data Structure Raw Processed Purpose of Data Not Yet Determined Currently In Use Users Data Scientists Business Professionals Accessibility Highly accessible and quick to update More complicated and costly to make change Please read more here https://www.talend.com/resourc...
CEO at WInterCorp LLC
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Many of the comparisons of data lake and data warehouse that you see (such as the one below from Talend) are based on an out-of-date or dumbed-down idea of the data warehouse.   The more advanced data warehouse engines: - support a wide range of data types and formats - can access external data (e.g., in object storage) that has never been ingested - support data scientists as well as business users (e.g., with an ability to run Python, R, SAS routines and data science libraries on data in place in parallel in the data warehouse) - support operational query on live, rapidly changing data While also providing capabilities and services never provided on data lakes or their cloud-based equivalents.  Data warehouses, properly operated and housing data that is properly curated, are much more efficient, cost-effective and performant for data that is intensively shared and widely used. Data lakes are good repositories for data that is more lightly or locally used and does not merit the level of curation usually desired in a data warehouse.
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