In the Beginning there were Online Transactions processing systems(OLTP) ie Operational Systems running Business applications along with the data stored in these systems. Whenever insights were needed, users had to perform queries to run analytical reports on these systems, which was too much overhead on the OLTP systems and the Operations team.
Then We had data warehouses/Analytical systems (OLAP) that worked into laborious data mapping from source to target resulting as in single source of truth. But these models took too long to build and tedious ETL developments were a pain. And they were even harder to change when the requirements changed. Moreover the limitation to use only structured tabular data. In the ever changing environment, this turned to be too slow in todays times.
Then came Data Lakes: Trend was to go on a rampage of ingesting data into the lake – a simple copy of production data from all conceivable sources if you will – with little thought about its use down the road. Unfortunately, simply ingesting data and making it available doesn’t work, just creates additional data asset that IT has to manage, care and feed going forward.
Data Lakes offers the flexibility to ingest variety of data be it Structured or Unstructured form of any volume. Ingesting alone is not enough, Its important to be able to curate it into a form that the business can readily use with complete trust. When data is coming at you that fast, Business must have a nimble though robust process to absorb that fast data effectively.
The true benefit of a data lake comes from making the data usable so that value enabled business decisions can be made.
The Data Lake store unstructured, semi-structured and structured data on a cloud. It is built for the enterprise, with no restrictions on the data size thus supports big data.
USing Hadoop applications, highly optimize data lake store can be used for data analytics. Main purpose is to store big data, support Hadoop applications, and utilized for hightly optimized data analytics.
Data is value for a range of businesses. Maintaining data in its native format ie documents, logs, feeds, media, etc and make them accessible for analytics and multiple processing purposes is the right solution for extracting value from data over time.
Analytics applications such as Hadoop, Spark and the ecosystem tools (Hive, Impala, etc use Data lakes for fast access to all data formats. Data Lake gives an unrefined view of data to data scientists as there is no need to model data into an enterprise-wide schema with a Data Lake.
Enterprises now use technologies capable of addressing a variety of applications, use cases, and business needs. Thus, these technologies must have the ability to collect data from a wide range of sources, formats, Internet of Things (IoT) data, and data collected from other internal and external sources. Then the data must be must be brought together often involving the combinations of the following.
Data-driven artificial intelligence and ML software used to empower smart systems
High Performance Servers and storage to support all of the above mentioned elements
This challenges the enterprises to cope with growing numbers and types of data management systems and ever more complicated schemes for processing the data.
Data Lake Analytics is an on-demand cloud analytics service where parallel data transformation and processing programs can be run using R, Python, U-SQL, and .NET over Petabytes of data.
Data Lake Analytics is optimized to work with Data Lake Store without worrying about which VMs, servers or clusters so the focus can be on jobs rather then infrastructure.
With the onset of storage engines like Hadoop etc storing disparate information has become easy.
Big Data Hadoop services by Bitics help your business easily access new data sources and tap into many types of data forms. Hadoop can store and distribute very large sets of data across many low price servers on a distributed file systems operating in parallel for high end speed processing requirements. It can efficiently process terabytes of data in minutes.
Hadoop’s architecture allows you to collect and diagnose extremely large amounts of data from disparate sources. Data sources include internal systems ERP’s, Legacy Databases etc as well as external systems such as social media and other publicly available platforms. With Big Data Hadoop analytics, we can refine and enrich enterprise data and make much more informed decisions. We provide Hadoop environment inclusion within your enterprise which includes proper scoping of the required architecture for analytics, setting up your Hadoop repository and loading sample data sheets as per specific use cases.
Insights that gives you the brand analytics, measurement, and consumer insights you need to grow your brand. Our technology transforms publicly available data into actionable intelligenc, helping you to find your brand value. It is a deep-web listening tool that uses machine learning and artificial intelligence to assess and prioritize risk.
Here at Bitics, we are convinced that the future belongs to those who can transform, refine and interpret large amounts of data into intelligent insight.
These insight can be acted on to create new business opportunities, stronger business decisions and automated solutions.
This covering the set-up, configuration, maintenance, monitoring, incident resolution and support activities for a company’s Hadoop Data Lake environment. Our Level1 Support includes:- monitoring, incident logging, automated alerts, and common incident resolution. Level2 and Level3 can be defined.
We start with a defined Transition Plan that could span a 3- to 6-month period, based on the scope of the managed services agreement and the complexity of the environment.
During this transition period, Right operating procedures and communication processes are established, and some of them are activated. We prefer the Operating Procedures to be of ITIL industry standards.
Automation for system monitoring, incident logging, alerts, and incident resolution should be introduced and established.
We will prepare a onshore-offshore team model, with plan for onsite rotation plan.
SLA driven performance will be used to measure compliance and variances.
Automated metrics tracking and reporting is a part of the standard weekly status reporting.