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.
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
Why we are the best Data Warehouse Solution for Big Data?
Data Lake UtilizationSet-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 IntegrationScalable, 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 targetsA2B Data™ is designed to leverage and process a variety of data streams from any source to any target database.
Supports multiple files and databases
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.
FastOur 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.
EconomicalOur 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.
AccurateJust 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.
SecureIt 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 HERE
What is Data Staging?
Staging database basicsA 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 ServerPDWWhen 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 benefitsThe 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 structuresThe 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.
PermissionsRequires 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.
The log size is typically similar to the replicated table size.