Why we offer the best San Diego Data Staging Solutions for Big Data?
A2B Data™ is a total, “Active” San Diego data staging solution for Big Data. Take your data from multiple sources into one unified place using point-and-click technology.
Are you in constant search for the best San Diego Data Staging Solutions for your business, well now you have found it. Furthermore, we can take your data from various sources using our premier “point-and-click-techno logo
Benefit Snap Shot
- Automates the Data Staging process
- Process data (bi-directionally) from “any source to any target”
- Saves money, as no maintained coding
- Scalable as it supports your small to very large data environments
- Accelerated project timelines
- Mitigated Project risks
- Preserved history of changed data
- Enforced 100% Accuracy and consistency
Production Use
Why we are the best Data Warehouse Solution for Big Data?
Data Lake Utilization
Set-up your data lake and begin ingesting big data in hours, not weeks. Our process is bi-directional and your data products can be extracted from the data lake. This data is in-turn, placed in other data stores.Cloud Integration
Scalable, Cloud-based Software-as-a-Service (SaaS) that Integrates your data across diverse environments seamlessly. Securely processes your data hosted on-premise, in the cloud, remote location or file storage.One solution, multiple targets
A2B Data™ is designed to leverage and process a variety of data streams from any source to any target database.
Supports multiple files and databases
Any delimited file, any structured and unstructured table formats, streaming and/or messaging data.
Quick Product Video
“What puts us ahead of the rest is how A2B Data™ processes your data.”
Our Experience in Automated Data Migration and Integration
Automated Data Migration and Integration Solutions for Big Data Environments
For over 21 years, our partner, Wyntec, (What You Need Technologies), has been a leader in big data solutions. From data architecture and integration to data mining, warehousing, migration and business intelligence, we have grown to be a powerhouse for providing Big Data.
Accelerate the Entire Process
Now with A2B Data™, Wyntec offers the fastest solution to automate you big data from any source to any target.
Why Us?
Fast
Our San Diego data staging solutions offer lightning fast deployment of data achieved from Source A to Source B. This helps push the data in minutes or hours.Economical
Our San Diego data staging solutions are designed to streamline the data acquisition process. Drastically reduce expensive man hours, reducing the overhead traditionally associated with data warehousing.Accurate
Just as important, one of the goals, are to always ensure that 100% data accuracy is achieved. As a result, you will see higher client retention & satisfaction. To help drive this critical process, our services includes elements like metadata driven utilization. You can feel confident, that these are all built-in best practice design patterns.Secure
It is our mission, to always keep your confidential data confidential, this is our commitment to you. With our San Diego data staging services, your information never leaves your firewall. Not quite what you are looking for? CLICK HEREWhat is Data Staging?
Staging database basics
A staging database is a user-created database that stores data temporarily while it is loaded into the appliance. When a staging database is specified for a load, the appliance first copies the data to the staging database and then copies the data from temporary tables in the staging database to permanent tables in the destination database. When a staging database is not specified for a load, SQL creates the temporary tables in the destination database and uses them to store the loaded data before it inserts the loaded data into the permanent destination tables.SQL ServerPDW
When a load uses the fastappend mode, SQL ServerPDW skips using temporary tables altogether and appends the data directly to the target table. The fastappend mode improves load performance for ELT scenarios where data is loaded into a table that is a temporary table from the application standpoint. For example, an ELT process could load data into a temporary table, process the data by cleansing and de-duping, and then insert the data into the target fact table. In this case, it is not necessary for PDW to first load the data into an internal temporary table before inserting the data into the application’s temporary table. The fastappend mode avoids the extra load step, which significantly improves the load performance. To use the fastappend mode, you must use multi-transaction mode, which means that recovery from a failed or aborted load must be handled by your own load process. Staging database benefits The primary benefit of a staging database is to reduce table fragmentation. If a staging database is not used, the data is loaded into temporary tables in the destination database. When temporary tables get created and dropped in the destination database, the pages for the temporary tables and permanent tables become interleaved. Over time, table fragmentation occurs and degrades performance. In contrast, a staging database ensures that temporary tables are created and dropped in a separate file space than the permanent tables. Staging database table structures The storage structure for each database table depends on the destination table.- For loads into a heap or clustered columnstore index, the staging table is a heap.
- For loads into a rowstore clustered index, the staging table is a rowstore clustered index.
Permissions
Requires CREATE permission (for creating a temporary table) on the staging database.Best practices for creating a staging database
- There should only be one staging database per appliance.
- The size of the staging database is customer-specific. Initially, when first populating the appliance, the staging database should be large enough to accommodate the initial load jobs. These load jobs tend to be large because multiple loads can occur concurrently.
When creating the staging database, use the following guidelines.
- The replicated table size should be the estimated size, per Compute node, of all the replicated tables that will load concurrently. The size is typically 25-30 GB.
- The distributed table size should be the estimated size, per appliance, of all the distributed tables that will load concurrently.
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The log size is typically similar to the replicated table size.
Staging Intent
People tend to define staging in relation to production. “Staging is where you deploy code before you deploy to prod.” “Staging is like prod but without customers.”“Staging is prod lite.” Staging can be all of these things, but let’s clarify its intent.Staging is where you validate the known-unknowns of your systems.
These known-unknowns are the dependencies, interactions, and edge cases foreseeable by the humans in your company and the machines they tend. Staging is where you gain confidence in your systems by consensus.Why staging?
Why have a staging environment? It’s easy to brush this off by saying “best practices,” but I think it’s good to examine best practices from time to time and make sure they actually fulfill your needs. Let’s define our needs as establishing confidence in our code, infrastructure, product, and deployment pipeline to ensure better stability of our platform. Can we fulfill these needs in other ways? Perhaps! To dig into this, let’s address the main argument against using a staging environment: tests.Staging & Testing
“You don’t need staging when you have good tests.” I’ve heard this from small startups and from companies that are nearly household names. These companies have two or two and a half environments: local development, an elaborate testing framework, and production. The testing framework is impressively robust and is fixed fairly quickly if it breaks. Passing builds are deployed to production with all the confidence green Jenkins jobs can buy. This process balances the edginess of the Cowboy Coder with the warm fuzzies of We Did All We Could.The Good, Bad Ugly about Testing?
But there’s an inherent problem with this model: It depends on the forethought of individual humans. Tests are good, but tests must be written. And who is writing them? Often, the person who wants to ship that section of code as soon as possible. Even if you write tests in pairs, that’s only two humans trying to account for every probable interaction of code in the wild. That’s not setting anyone up for success.UI Testing
This model breaks down further when your product has a UI. People don’t write unit tests to ensure a sidebar is the proper shade of millennial pink. Mockups only go so far, and not everyone can make design meetings or stay focused in them. Running your UI in an environment where employees have to look at and interact with it smokes out issues from color mismatches to weird button behaviors.UI Testing Environment
Even without a UI, tests don’t account for all possibilities. Asking one or two humans to imagine the innumerable interactions of machines isn’t likely to produce good coverage. But an environment where those interactions exist and you can let the code run organically likely will. Enter staging environments.
If you are developing a web site or an application, your workflow will usually include at least three environments: Development, Staging and Production.
