Your Data is your power
Data Integration Services
Data is one of your most valuable assets and drives opportunities throughout your organization. Integrating data from even disparate systems doesn’t have to be a headache now, when you’ve got an accurate data integration service to rely on. For example:
- Access Data from any data source
- Extract, Transform, Load and consolidate exactly what you need
- Ensure the Quality of data that is fit-for-purpose
- Ensure the Data Delivery on demand
- Create maximum value for your organization from your data.
- Get your data in exactly the format that suits you.
- Focus on your business while we run your data.
Delivering the right data on time is not a challenge now through the Data automation. This is the process of updating data programmatically, rather than manually. Automating the process of data uploading is important for the long-term sustainability of your open data program. Any data that is updated manually risks being delayed because it is one more task an individual has to do as part of the rest of their workload. There are three common elements to data automation: Extract, Transform, and Load, or ETL/ELT. Each one of these processes is critical to fully automating your data uploads, and doing it successfully. Either you are on Central, or Hybrid or Distributed model. It is important to determine a general data automation strategy for your organization. Having a strategy beforehand will help you engage the right people at the right time.
The control on data is the key to optimizing your business strategies. Usually organizations store massive amount of on different platforms, such as ERP, CRM, spreadsheets … This causes the loss of data traceability, so the organization cannot get a global view that allows it to make better strategic decisions. Currently, storing data is not enough for the organization to have greater competition, but it is necessary that the data are integrated in a single place so that they cease to be a cost to become a business asset. To achieve this, the organization must carry out an ETL process. We are supporting all type of tools: For example:
- Enterprise: Used by companies that have a larger size, higher cost compared to other options available. Oracle Data Integrator, SAP Data Services, IBM Infosphere DataStage, SAS Data Manager, Microsoft SQL Server Integration Services – SSIS.
- Custom ETL Programming: Companies that develop their own tools in order to have greater flexibility in Java, .Net, Python, etc.
- Open Source: Free open source tools for all users. Examples: Pentaho Data Integration, Talend Open Studio.
- Cloud Service: Tools from Google, Microsoft or Amazon that have their own ETL services in the Cloud. Examples: Amazon AWS Glue, Microsoft Azure Data Factory, Google Cloud Dataflow, Amazon AWS EMR.
Structured Database Services
A relational database, like Oracle Database, MySQL, PostgreSQL, MS SQL, DB2 etc…, is a data repository to stores information in structured tables with rows and columns. Because data is stored in a structured way, it can be retrieved using a query language that understands the table structure. Structured database has following features:
- Ensuring database transactions are processed reliably (Atomicity, Consistency, Isolation, and Durability)
- Referential integrity ensures that relationships between tables remain consistent
- Fine-grained locking or synchronization mechanisms for managing simultaneous access of the same data by multiple users
- Support for Unicode for multilingual capability
- Ability to run database seamlessly on multiple platforms
- Ability to recover data in the event of a failure
We support on-prim, Cloud and Hybrid model for structured database services.
Unstructured Database Services
To manage your organization unstructured data a none relational database like Cassandra, NoSQL, Hadoop, MongoDB is required. For examples e-mail messages, word processing documents, videos, photos, audio files, presentations, webpages and many other kinds of business documents. Note that while these sorts of files may have an internal structure, they are still considered “unstructured” because the data they contain doesn’t fit neatly in a database.
- Data neither conforms to a data model nor has any structure.
- Data cannot be stored in the form of rows and columns as in Databases
- Data does not follows any semantic or rules
- Data lacks any particular format or sequence
- Data has no easily identifiable structure
- Due to lack of identifiable structure, it cannot used by computer programs easily
On-prim, Cloud and Hybrid all model are supported for unstructured database services.
Data Ware House Services
DWH is the backbone for Analytics and Business Intelligence for your organization. Your data warehouse is like the foundation for the optimize business strategy. It needs to be sound to support everything in it. Analytics should never be an afterthought when it comes to housing your data. It can help you realize a great return on investment and ensure the foundation of your business is in top-notch shape. We can help you analyze your business needs to drive the design of your data warehouse, including a solid data model, business intelligence framework, smart database and an efficient data integration architecture so that it’s optimal for analytics and business intelligence.
Business intelligence can help companies make better decisions by showing present and historical data within their business context. Analysts can leverage BI to provide performance and competitor benchmarks to make the organization run smoother and more efficiently. Analysts can also more easily spot market trends to increase sales or revenue. Used effectively, the right data can help with anything from compliance to hiring efforts.
We can help you to achieve the following goals:
- Data mining: Using databases, statistics and machine learning to uncover trends in large datasets.
- Reporting: Sharing data analysis to stakeholders so they can draw conclusions and make decisions.
- Performance metrics and benchmarking: Comparing current performance data to historical data to track performance against goals, typically using customized dashboards.
- Descriptive analytics: Using preliminary data analysis to find out what happened.
- Querying: Asking the data specific questions, BI pulling the answers from the datasets.
- Statistical analysis: Taking the results from descriptive analytics and further exploring the data using statistics such as how this trend happened and why.
- Data visualization: Turning data analysis into visual representations such as charts, graphs, and histograms to more easily consume data.
- Visual analysis: Exploring data through visual storytelling to communicate insights on the fly and stay in the flow of analysis.
- Data preparation: Compiling multiple data sources, identifying the dimensions and measurements, preparing it for data analysis.
For Further details or to discuss please reach us.