We’ll help you define and implement a best-fit, modern architecture to ingest, process and enable your enterprise data analytics.
Data Architecture. Proven Solutions, Tailor Fit.
Selecting and implementing the right data analytics architecture has never been more challenging. Increasingly fewer organizations can simply get started by picking a database and ETL technology. Data platforms and programming technologies are proliferate. Modern data architects must integrate a portfolio of technologies to optimize data ingestion, storage, access, processing and analytics — all while considering overall cost and development team productivity.
Defining custom architectures…expertly, pragmatically
We recognize that you may have made substantial data warehousing investments. So, we don’t employ a “cookie-cutter” architecture, rather we consider your environment, designing a customized architecture that best fits your unique requirements. We do this by asking the right questions, educating you on best-practice options and helping you put together an optimal solution.
We’ll help you answer the detailed questions.
- Do you need Hadoop? Cloud or on-premise? Which distribution? Which processing engines will you utilize? Spark? Map-Reduce? How will you ingest data? Flume? Storm? HDFS commands?
- Should you program with Java, Pig, Hive, Python, Cascading and/or Scala?
- What about SQL on Hadoop options? Should you consider Impala? Stinger? SparkSQL?
- Should you consider one of the myriad purpose-built analytic databases like Actian Vector and Matrix, InfiniDB, MonetDB, Redshift? How does this technology integrate with Hadoop?
- How do you leverage existing investments in Netezza, Teradata or Greenplum?
- What to do with your existing Oracle/SQLServer/DB2 data warehouse? Should you replace your data warehouse with Hadoop?
- Can you leverage existing ETL investments in Informatica? Pentaho Data Integration? SSIS?