As disparate data volumes continue to be operationalized across the enterprise, data will need to be processed, cleansed, transformed, and made available to end users at greater speeds. Traditional ODS systems run into issues when trying to process large data volumes causing operations to be backed up, data to be archived, and ETL/ ELT processes to fail. Join this breakout to learn how to battle these issues.
Data storage costs: http://thecaucus.blogs.nytimes.com/2012/08/14/advances-in-data-storage-have-implications-for-government-surveillance/IoT: http://www.forbes.com/sites/gilpress/2014/08/22/internet-of-things-by-the-numbers-market-estimates-and-forecasts/
Resource Intensive ELT: http://www.syncsort.com/getattachment/45696aa9-1e40-43cb-8905-b9fc7e2519f7/Syncsort-Data-Warehouse-Offload-Solution.aspx
An Operational Data Store provides a staging environment in order to ingest, store, and process data in preparation for operational and analytical use.
Depending on whether or not this data is structured or unstructured, different systems can be used to optimize data pipelines.
The only challenge is that as your organization continues to ask for larger volumes of diverse data, traditional systems face issues.
These challenges specifically arise around data storage and processing.
The first challenge is limited data access. Collecting and ingesting a wide variety of diverse data is not a simple task and usually results in additional systems, or capacity being added to the architecture. As the business continues to ask for more data this continues to put strains on IT. In order to avoid these challenges only the most valuable data is brought in, limiting the businesses access to data that could be extremely valuable.
The second challenges that we see organizations try to hurdle is around processing data volumes. These organizations have already collected and operationalized large volumes of data and need to process this data efficiently in order to meet SLAs. If data doesn’t reach the employees in a timely manner then they continue on without the most recent information.
The third and final set of challenges is around archiving data. When systems reach capacity as larger volumes of diverse data is leveraged within an organization, this causes IT professionals to archive or delete data that has been deemed “invaluable”. When data is moved offline to an archive, this significantly reduces the return on the data and can hurt the business. This data can be extremely important as analyst attempt to find patterns in historic data but can’t access this information because it’s offline.
However, as the external and internal data environment has changed over the years so has the data management space.
These challenges specifically arise around data storage and processing.
The first challenge is limited data access. Collecting and ingesting a wide variety of diverse data is not a simple task and usually results in additional systems, or capacity being added to the architecture. As the business continues to ask for more data this continues to put strains on IT. In order to avoid these challenges only the most valuable data is brought in, limiting the businesses access to data that could be extremely valuable.
The second challenges that we see organizations try to hurdle is around processing data volumes. These organizations have already collected and operationalized large volumes of data and need to process this data efficiently in order to meet SLAs. If data doesn’t reach the employees in a timely manner then they continue on without the most recent information.
The third and final set of challenges is around archiving data. When systems reach capacity as larger volumes of diverse data is leveraged within an organization, this causes IT professionals to archive or delete data that has been deemed “invaluable”. When data is moved offline to an archive, this significantly reduces the return on the data and can hurt the business. This data can be extremely important as analyst attempt to find patterns in historic data but can’t access this information because it’s offline.
However, as the external and internal data environment has changed over the years so has the data management space.
These challenges specifically arise around data storage and processing.
The first challenge is limited data access. Collecting and ingesting a wide variety of diverse data is not a simple task and usually results in additional systems, or capacity being added to the architecture. As the business continues to ask for more data this continues to put strains on IT. In order to avoid these challenges only the most valuable data is brought in, limiting the businesses access to data that could be extremely valuable.
The second challenges that we see organizations try to hurdle is around processing data volumes. These organizations have already collected and operationalized large volumes of data and need to process this data efficiently in order to meet SLAs. If data doesn’t reach the employees in a timely manner then they continue on without the most recent information.
The third and final set of challenges is around archiving data. When systems reach capacity as larger volumes of diverse data is leveraged within an organization, this causes IT professionals to archive or delete data that has been deemed “invaluable”. When data is moved offline to an archive, this significantly reduces the return on the data and can hurt the business. This data can be extremely important as analyst attempt to find patterns in historic data but can’t access this information because it’s offline.
However, as the external and internal data environment has changed over the years so has the data management space.
We have been working closely with leading organizations to create a platform that allows them to complement their current architecture in order to avoid these common challenges. This in turn prepares for future growth of data within their organizations.
Ingest More Data-
Cloudera allows you to collect and ingest any data type or volume of data, in full fidelity, in order to allow for complete data access to your current systems and end users. This has allowed organizations to collect and access more diverse data, opening up the possibilities of what data can do for the business, without compromising system performance or existing resource constraints.
Efficiently Process & Store Data Volumes-
By offloading heavy processing workloads to Cloudera, organizations are able to use parallel processing in order to significantly reduce processing time on large volumes of data. With the scalable nature of Cloudera, you also ensure that no matter how much data is stored the platform continues to perform at peak performance.
Automated Secure Archive-
Leveraging Cloudera as an ODS and using it as a centralized staging environment for new data allows you to automatically create a secure archive. Because of the platform’s scalable nature, there is never a reason to archive your data. Historic data can remain on the platform for analysts allowing them complete access without derogating system performance. While smaller volumes of already defined active data can run directly into the right systems, with outdated data being offloaded to Cloudera.
Leading data organizations have already seen these benefits.
We have been working closely with leading organizations to create a platform that allows them to complement their current architecture in order to avoid these common challenges. This in turn prepares for future growth of data within their organizations.
Ingest More Data-
Cloudera allows you to collect and ingest any data type or volume of data, in full fidelity, in order to allow for complete data access to your current systems and end users. This has allowed organizations to collect and access more diverse data, opening up the possibilities of what data can do for the business, without compromising system performance or existing resource constraints.
Efficiently Process & Store Data Volumes-
By offloading heavy processing workloads to Cloudera, organizations are able to use parallel processing in order to significantly reduce processing time on large volumes of data. With the scalable nature of Cloudera, you also ensure that no matter how much data is stored the platform continues to perform at peak performance.
Automated Secure Archive-
Leveraging Cloudera as an ODS and using it as a centralized staging environment for new data allows you to automatically create a secure archive. Because of the platform’s scalable nature, there is never a reason to archive your data. Historic data can remain on the platform for analysts allowing them complete access without derogating system performance. While smaller volumes of already defined active data can run directly into the right systems, with outdated data being offloaded to Cloudera.
Leading data organizations have already seen these benefits.
We have been working closely with leading organizations to create a platform that allows them to complement their current architecture in order to avoid these common challenges. This in turn prepares for future growth of data within their organizations.
Ingest More Data-
Cloudera allows you to collect and ingest any data type or volume of data, in full fidelity, in order to allow for complete data access to your current systems and end users. This has allowed organizations to collect and access more diverse data, opening up the possibilities of what data can do for the business, without compromising system performance or existing resource constraints.
Efficiently Process & Store Data Volumes-
By offloading heavy processing workloads to Cloudera, organizations are able to use parallel processing in order to significantly reduce processing time on large volumes of data. With the scalable nature of Cloudera, you also ensure that no matter how much data is stored the platform continues to perform at peak performance.
Automated Secure Archive-
Leveraging Cloudera as an ODS and using it as a centralized staging environment for new data allows you to automatically create a secure archive. Because of the platform’s scalable nature, there is never a reason to archive your data. Historic data can remain on the platform for analysts allowing them complete access without derogating system performance. While smaller volumes of already defined active data can run directly into the right systems, with outdated data being offloaded to Cloudera.
Leading data organizations have already seen these benefits.