Data Services:
administrative process of acquiring, validating, storing, protecting, and processing information.
Data management is the practice of managing data as a valuable resource.
Managing data effectively requires having reliable methods to prepare data for analytics.
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Enterprises are making use of Big Data more than ever before to inform business decisions and gain deep insights into customer behavior, trends, and opportunities.
- Data Ingestion: incorporates internal and external data sources
- Data Bases: handles structured and unstructured data files and types
- Data Lakes: central repository hosting companywide data assets
- Data Warehouses: ready for consumption stores of data
Data Management as an overall practice is involved with the entire lifecycle of a given data asset from its original creation point to its final retirement, how it progresses and changes throughout its lifetime through the internal (and external) data streams of an enterprise, and how it is discarded with sound and compliant lifecycle policies.
Data Ingestion:
Movements of InformationMind the Costs: The movement of information can come with hidden charges and surcharges. Public cloud providers brag about the low cost of data storage, yet data movements come with premium pricing. Before a single byte of data is moved to the cloud, a sound transfer and movement strategy must be developed and validated. There are instances of where using a larger transfer bus, is more cost effective than using a smaller bus. Don't forget to mind the costs.
Extract, Transform, Load (ETL): The process of bringing-in data, converting-it, and placing-it at the correct location. A necessary step in any cloud implementation. This is the most-common source of team pain, project cost overrun, and collective frustration. Experienced engineers will tell you that a correct ETL makes the whole development process a breeze to enjoy; while a bad ETL will cause delays, overruns, and is a sure way to increase a project's demise.
Exports to 3rd Parties: Once your application or data process completes, then the output is sent to the correct location. The location can be on the cloud or on-premise, internal or external, private or public, and many more. The exact place, method, and type in which the output is placed is determined by the application or process involved.
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Data Bases:
Organized and Efficient
Relational vs NO-SQL:
Structured vs un-structured; what does it mean?
In a simple non-technical way think of it as team uniforms. Are all players wearing the same hats, shirts, pants, belts, and shoes?
If so, that's more along the relational database lines; in which data comes as expected. Yet, sometimes, teams don't come with same uniforms,
in these scenarios, of more diverse row and column types, we call them semi-structured or un-structured (depending on how messy combinations).
Ad-Hoc Queries:
Our cloud based data bases can be accessed, modified, managed, controlled, and organized to perform processing operations.
The data is indexed (organized) across rows, columns, and tables that make workload processing and data querying efficient.
Design your own custom queries to cut and slice the data as you wish; share your queries with colleagues; publish to the world.
Disaster Recovery & Backup: We design data bases that have multiple availability zones, which means that even if an electrical failure occurs, your business will still be running since a backup system from a different geographical region kicks-in. Data is backed-up for full and easy recovery. However, there are situations outside of anyone's control, in which data is lost. For those lost scenarios, an automated process captures data-loss instances and retrieves data packets from source or from network-transit buses. Our systems are designed for full disaster recovery and backup. We offer clients strict Service Level Agreements (SLA) that ensure their systems are operational and can come back to recover on their own volition. Alerts and notifications are sent in case of any triggered data recovery or backup.
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Data Lakes:
Central Location with Complete ControlWhat is a Data Lake? Non-Technical Our friend James Dixon from Pentaho used the following analogy to famously coin the term: “If you think of a datamart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.” At Data Glue we develop world-class data lakes for any enterprise size.
What is a Data Lake? Technical
A data lake is a centralized, curated, and secured repository that stores all company data, both in its original form and prepared for analysis.
A data lake enables a company to break down data silos and combine different types of analytics to gain insights and guide better business decisions.
The lake can set access and control rules with, if allowed, object-level security capabilities, which limit the contents of the data allowed to be shared.
The lake enables complete oversight and control over EVERY piece of data.
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Data Warehouses:
Ready for Consumption Stores
Warehousing your data ready for use:
A data warehouse works by organizing data into a schema that describes the layout and type of data, such as integer, data field, or string.
Making what was once unstructured into a highly organized and optimized format for your end users to consume.
Bulk write operations typically on a predetermined batch schedule so you can do many transformations quickly, all at once, using distributed systems.
Made for quick distribution of data.
Features:
- Data Catalog
- Data Lake Integration
- Best Performance
- Easy to Manage
- Machine Learning Ready
- Secure and Compliant
- Global Reach
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