Comprehensive big data solutions
Whether you‘re just starting with big data or simply need assistance implementing new requirements, Inquidia is ready to help. Our big data implementation services experts will assist you in the planning, requirements, architecture, design and implementation of big data technologies to support your business analytics goals. Inquidia can both conceptualize and implement solutions that orchestrate and execute the entire process of big data management.
We do core Hadoop engineering.
Hadoop is now the standard for dealing with big data, and, at this point, is quite mature. Many of our clients have deployed Hadoop to meet their scalable storage, processing and, increasingly, query needs. It is not, however, just one thing -- it is a giant ecosystem of tools and technology which solve a wide variety of problems.
For the uninitiated, Hadoop, itself, is a distributed filesystem and framework for distributed computation. The Hadoop ecosystem, however, is vast and contains a variety of tools which interface with Hadoop and/or solve problems related to dealing with big data, for example tools for ingesting data from conventional RDBMS, process orchestration, cluster management, monitoring, query, various distributed databases, libraries for machine learning, and so-on. We can help you choose the right set of tools from the ecosystem which will meet your needs.
We can help you engineer a Spark solution
Spark, a recent product of academic research at Berkeley, opens many new possibilities for the Hadoop ecosystem. Namely, some iterative algorithms which were previously out-of-reach can be implemented and real-time results for some sorts of queries are now possible. The magic of Spark is its storage abstraction which can store intermediate results in-memory for the duration of a process, spilling to disk as necessary. We think the Spark ecosystem is the next "big thing" for big data, and we can share our observations about advantageous use cases for Spark with you.
Ingesting your data into these kinds of architectures requires certain skills. We've got them.
How your data is organized and structured will effect the complexity and performance of processes and queries needed to access it. Organization of data in a data store will always matter, even if it's big.
Some patterns applicable to conventional RDBMS-based data warehouses (eg., star schemas, columnar organization of data, Kimball dimensions, and so-on) are every bit as relevant in big data. However, some of them (consider slowly changing dimensions) take on new complexity in the context of big data -- where file content is essentially immutable. We have experience with a variety of patterns in the big data context and are constantly generating new best practices as we adopt new big data innovations.
We'll help you implement the right kind of programming model.
Ubiquitous Hadoop ecosystem tools, such as Hive and Pig, provide traditional block-and-tackle data manipulation; however, sometimes you will want to do fancier things. There are a variety of innovations including Cascading and Spark that give you options. Traditional ETL tools, like Pentaho Data Integration, are also innovating to run "in-cluster". The future of big data programming is bright, changing rapidly to meet enterprise demands.
Reporting on big data is a little different, but we're on top of it.
So you've ingested and processed your big data, now what? Well, your users want to query it and your data scientists want to analyze it. We know how to make both audiences happy.
The growing maturity of SQL on Hadoop options including Impala, SparkSQL, Hive/Stinger and embedded proprietary databases is beginning to make interactive query on big data a reality. We can help you choose, implement and optimize the right solution. Alternatively, there is nothing wrong with pulling data out of Hadoop and into purpose-built analytic databases. We can help you do that too.
Optimizing these architectures can be a little tricky. We know how.
We’ve helped clients deploy Hadoop on premise, and in the cloud. We have experience with a variety of distributions, and can recommend distribution, deployment strategy, and cluster size based on your data volume, retention policy, governance needs, availability needs, and anticipated computational needs.