Postgresql, Oracle, SQL Server, Mysql, MariaDB – Database Infrastructure
We provide expertise for the full lifecycle of your databases, leveraging over more than 20 years experience in database infrastructures.
We provide the following expert services :
- Software Development Support Service: we are very experienced supporting development teams with the technical requirement specifications, database design and performance assessment. We provide advice on development tools and processes to improve the efficiency of development teams.
- Performance Tuning Service: we deliver performance audit services and carry on short-term, “warfront” business-critical production performance troubleshooting missions on-site or remotely.
- Migration & Consolidation Service: we migrate your databases within to new servers either on-premise or to the cloud. We execute cross-platform migrations (for example, from Windows to Linux). We consolidate multiple databases on a single server and help you size your servers for optimal utilization.
- Upgrade Service: we upgrade your database versions and propose an upgrade strategy that suits your availability requirements.
- Audit Services: we provide a range of audit services (see below for more information).
- Consulting Services: we provide services that help operational management make informed decisions in the scope of infrastructure evolution, data protection and risk management. See our Management & Strategic Consulting Services page for more details.
In a nutshell :
- 8.x & 9.x
- Upgrade and migration
- Cloud IaaS deployment : Amazon EC2, Microsoft Azure, Google Cloud
- Cloud DBaaS : Amazon RDS, Alven, EDB Postgres Plus
- Pgpool-II (HA & load balancing.)
- 9i, 10g, 11g, 12c
- on-premise or on the cloud : DBaaS (Amazon RDS, Oracle Cloud).
- Real Application Clusters (RAC) 11g+
- Solaris, Linux, IBM AIX and MS Windows.
- Mysql 4.x, 5.x
- MariaDB 10.0, 10.1, 10.2
- On premise, or on IaaS (Amazon AWS, MS Azure, Google Cloud) and DBaaS (Amazon RDS, Oracle Cloud)
- SQL Server 2008, 2012, 2014 and 2016
- On premise or on the cloud : DBaaS/IaaS (Amazon RDS, Microsoft Azure)
- Expertise with In-Memory OLTP features in SQL Server 2016 (Memory-optimized tables and Natively Compiled Stored Procedures) to achieve extremely low latency application performance.
Sensor & Metric Databases
We have a good expertise deploying and maintaining InfluxDB and prometheus databases.
Our experience includes monitoring and IoT scenarios with the InfluxData TICK stack (Telegraf, InfluxDB, Chronograf and Kapacitor) as well as Grafana integration with InfluxDB and prometheus.
We provide standardized audit services for all the RDBMS we support. The specific tools and techniques used may vary from system to system, but the methodology remains the same and is the fruit of years of experience delivering such services. Below you’ll find our Audit Service catalogue.
At a second stage, we analyze the SQL statements and the application architecture in order to improve the application performance (SQL tuning and middleware). We work in a controlled test environment and deliver measured and verified recommendations for every test case.
- Is the database system coping well with the current workload ?
- Will the database system be able to sustain the workload for the following X years ?
- Can the database system grow smoothly ?
- Are there any limitations of the current technology that I must be aware of ?
- Is my database system future-proof ?
- Are our Capacity Planning processes solid ?
- Is my database system reliable ?
- Is the database architecture appropriate for the current/future workload ?
This is a punctual evaluation of the system that requires a good understanding of the business processes it supports and is targeted at IT management in need of a risk assessment of the current infrastructure assets.
We investigate data privacy issues when required, in order to identify encryption requirements and regulatory compliance.
Apache Spark, Apache Hadoop, Apache Kafka, Cloudera, Hortonworks – Big Data Infrastructure
Big Data Infrastructure
Big data technologies enable the application of data mining on large volumes of data. However, there are other scenarios where big data is effective.
The need for a “Divide and Conquer” approach emerges when your application can no longer handle the data volume and growth velocity. This situation is known as a Big Data problem.
There are different big data technologies you can leverage to let your application scale, they range from data sharding, in-memory databases, SQL on Hadoop, NoSQL databases and specialized big data components for search, graphs, text and so on. They may or may not involve the hadoop ecosystem : we make the best of both worlds, the traditional database technologies and the new big data technologies.
We can advise on deployment alternatives, if you don’t need or want to deploy your infrastructure in-house: private or public cloud and managed services are other options.
- Deployment Service: selection of state-of-the-art software upon requirement specitications. Installation and configuration of software on-premise or in the cloud.
- Data Engineering Service: support in the implementation of data pipelines. From ingestion to exploitation.
- Machine Learning Implementation Services: development of machine learning solutions leveraging your big data infrastructure.
- Apache HDFS
- Apache Parquet
- Apache Squoop
- Apache Kafka
- Apache Cassandra
- Apache Hbase
- Apache Tez
- Apache Hive
- Apache Drill
- Apache Impala
- Spark SQL