data warehouse modernization
data lake implementation
real-time stream processing
Our big data engineering services allow you to innovate, experiment with new tools, explore new ways of leveraging data, and continuously optimize your big data solutions.
Traditional warehouses were never designed to handle the growing volume, variety, and velocity of Big Data. As new business practices require bigger and fresher data, you might have already thought about modernization of your Data Warehouse to keep it competitive, growing, and aligned with new business and technology requirements.
Most Data Warehouses have room for improvement. Our team of world-class data engineers will help you accommodate massive data volumes, new data types, and new data processing workloads by adding data platforms and analytics tools to your Data Warehouse environment.
We can also help you with legacy system retirement, replacing traditional Data Warehouse with a modern one optimized for today’s requirements in big data, analytics, real-time operation, high-performance, and cost control.
Technical success depends on the team – our team can bring years of experience with big data engineering and analytics to your company, and ensure success of your Data Warehouse Modernization project.
To capture and get business value from new types of data you need to constantly develop your data storage, management, and analytics capabilities. The immediate modernization opportunity for many organizations lies in establishing a Data Lake.
A Data Lake simplifies the acquisition and storage of diverse types of data, whether structured, semi-structured, or unstructured.
A Data Lake is a foundation for data analytics, it can feed both the production BI/DW environment and analytics sandboxes for data analysts and data scientists.
A Data Lake can scale in a cost-effective manner.
Whether you want to analyze immense volumes of data recurrently or ingest and process streaming data in real time our data engineers will help you create a big data processing system tailored to your business requirements.
Batch processing is the right approach for high-volume, repetitive, non-interactive business tasks. But more often you need to analyze rapidly changing, multi-structured data in real time. Our big data engineering services will help you build the business-critical applications that can process large streams of live data and provide results in near-real-time.
We use open source technologies like Spark, Hadoop, Flink to enable advanced analytics and the real-time use cases that are driving business innovation.
Big Data applications deployment might be a challenging task as well. We help our customers to deploy their Big Data applications anywhere they choose: on-premise or in the cloud.
Transform an increasing amount of big data into business value without any risks.
Make the best decisions fast without getting overwhelmed by the volumes of real-time data coming in.
Ensure your data is available when and where it is needed.
Ensure your data warehousing solution captures all the essential data sources.
Developing an advanced analytics roadmap can become a frustrating challenge even for a cutting edge company. The process requires an overhaul of the data infrastructure.
Majority of the companies choosing that road need a substantial upgrade to their data platforms.
We take our clients through such transformations as replacing legacy systems with cloud-based solutions that support scalability, or replacing ETL with data management processes that support a move to real-time analytics.
Most companies use analytics, many of them can act on data from months, weeks, or even days ago, but very few of them can respond to changes minute by minute.
Real-time data processing and analytics technologies are helping companies quickly find useful information in streams of big data and make informed decisions to boost their business operations.
At InData Labs we help to build real-time stream processing systems that can analyze semi-structured, unstructured or geospatial data while coping with fast-changing shapes of it.
Traditional warehouses were never designed to handle the growing volume, variety, and velocity of Big Data. Data warehouses and requirements for them continue to evolve that’s why at some point companies face the need of aligning the data warehouse environment with new business requirements and technology challenges.
Our team of world-class data engineers will help you accommodate massive data volumes, new data types, and new data processing workloads by adding data platforms and analytics tools to your Data Warehouse environment.
Filling the data lake can be a very complex process requiring a comprehensive approach. A lot of organizations are currently being challenged by the diversification of data types and formats, plus the diversifications of data sources.
We start the process of filling the data lake with identifying the data sources and the type of data that can support specific decisions and business objectives. Most of the exotic data types poised for future growth are the least managed at the moment, so we make sure to take into account GPS, social media, GeoIP data, and etc.
|Data analysis & platforms||MapReduce, Hadoop, Cloudera, HortonWorks, Spark, Flink|
|Tools||Lucene, Solr, ElasticSearch, Apache Mahout, Apache Kafka, Apache Pig, Splunk|
|Databases/Data warehousing systems||MySQL, Oracle, MS SQL Server, Redis Couchbase, Hbase, Cassandra, MongoDB, Hive, Neo4J, Aerospike|
|Business Itelligence||Pentaho, Superset|
|Software Engineering||Java, Scala, Python, R|
|Visualization||SAS VA, Tableau|
From the case study, you will learn how we helped an influencer marketing agency build a social media analytics platform that delivers actionable insights in real-time.