Enterprises are always on the lookout for new approaches to accelerate data analysis to discover avenues for business growth. However, “most of the processes and architecture used by enterprises, are purpose-built and engineered for specific ingestion needs.” They are not flexible enough to handle the large volume and variety of data that is being added on to the system.
First step in this process is, Data ingestion to import or acquire data for immediate use or storage for further analytics. This data may be streamed in real-time or ingested in batches. Most systems have a disparate and customized transformation process, making data ingestion a complex activity. Not surprisingly, it ends up being a tedious, time-consuming and expensive proposition from both deployment and maintenance perspectives. Also, the time lag associated with the command line and coding dependent tools fetters access and timeliness of data availability.
So, why do enterprises need a robust data ingestion solution?
- The primary goal of creating a Big Data lake is to accelerate business insights that can be delivered to a wide range of users.
- Data lake facilitates managing, understanding data lineages and better governance of data.
- Adapting to newer data-based technology becomes easier to implement, as the required data is already residing in the data lake and ready for consumption.
- Flexibility to enable new ingestion pipelines to help foster new business needs and innovation with minimal efforts.
- A well-designed ingestion mechanism would reduce errors in the process and help in cost savings.
An effective data ingestion process is begun by prioritizing data sources, validating individual files and routing data items to the correct destination.Now, here’s where our expertise comes in..
We have designed and implemented Data lake ingestion for our numerous clients and have an excellent in-house expertise in this technique. Knowledge Lens’ MLens solution simplifies High-speed data ingestion, data migration, secured cluster provisioning, data compression, data archival, data masking, Big Data backup and Disaster recovery for the Enterprise Data Lake.
MLens performs high-speed parallel data processing onto cloud storages such as S3 or Azure Blob as well as standard RDBMS without the need of any landing zones. It has the unique capability of supporting compression and file format conversions during ingestion as well as the merging of small files on the fly. Automation plays a key role in the entire process and we have built an easy to use GUI which supports configuration driven transformation during ingestion.
MLens as a complete solution suite provides utilities for an Enterprise’s Big Data needs. We have addressed various Big Data business needs for our clients and a glimpse of these implementations are herewith.
What we do
- Migration of Data across secured Hadoop clusters and between Databases to Hadoop or between Databases
- Migration of Data across clusters with different Hadoop distributions, database Distributions and across versions
- Provide real-time Hadoop cluster Data Replication across secured clusters
- Provide real-time replication of HBase database
- Migration of data from Hadoop to S3 and vice-versa
- Backup of metadata and folder compare utilities across clusters and S3
- Distributed Data ingestion from SFTP/S3 to secured cluster
- Compress data, convert files to standard formats and merge files during data transfer
Enterprise grade security is supported through Security Integration through Active Directory or LDAP or Kerberos based Authentication for secure data access. Secured authentication for all endpoints and sources using integrated authentication has been built into the solution.
Enterprises have reaped benefits with our solution’s high speed compression and secure data transfer. They have been able to achieve a faster and higher order of backup efficiency. A well-orchestrated recovery of cluster and data is now available in minutes!
Visit us here to learn how you can grow your enterprise through data-driven decision making, starting today.